Predictive analysis and interventions to limit disease exposure

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for predictive analysis and interventions to limit disease exposure. In some implementations, user data indicating a prospective action of a user of a mobile device is received. Content is provided to cause the mobile device associated with the user to present a prompt for user input regarding the prospective action of the user. Potential future exposure of the user to a disease is evaluated based on response data indicating a response to the prompt. A disease exposure prevention option for the user is selected or customized for the user based on at least one of the user data or the response data. Content is provided to cause the mobile device associated with the user to present the disease exposure prevention option.

BACKGROUND

Infectious diseases such as COVID-19 can be challenging to track andtreat, especially on a large scale. Traditional techniques for detectinginfection and delivering treatment may not provide a sufficiently rapidor personalized response to effectively detect and treat disease in somecases. In addition, the factors that affect the spread and impact ofdisease can be variable from location to location. As a result,different communities may have different risk profiles and needdifferent strategies to prevent, contain, or otherwise manage the spreadof disease.

SUMMARY

In some implementations, a system provides a technology platform tomonitor, predict, and contain the spread of COVID-19 and other diseases.The system can evaluate the risks and needs of different communities tocustomize various disease-related measurements, predictions, andrecommendations for the different communities. In some implementations,the communities can be different geographic regions, and fine-grainedrecommendations can be made for areas such as counties, cities, zipcodes, or neighborhoods. Among the tools provided to communities, thesystem can predict areas that are likely to become disease hotspots,based on current behavior patterns of individuals in a community.Similarly, the system can predict disease measures (e.g., infections,hospitalizations, and so on) for future times based on a variety offactors, including real-time monitoring data. These and otherpredictions can be made using machine learning models that are generatedusing training data indicating monitoring data and outcomes over timefor many different communities.

In some implementations, a medical treatment device provide one or moretherapies for treating COVID-19 or COVID-19 symptoms. The medicaltreatment device provides features to perform data collection, includingmeasurement of one or more physiological parameters of a user andlocation tracking. The medical treatment device can be configured to uselocation tracking data to determine whether and when to initiatetreatment for COVID-19. For example, the medical treatment device canmeasure body temperature, heart rate, respiration rate, or otherparameters, and based on the measured parameters determine that the useris likely to have contracted COVID-19. In addition, the medicaltreatment device can determined, based on location tracking data such asGPS data, that the medical treatment device has been in one or moreregions that have elevated disease transmission potential for COVID-19,thus making infection with COVID-19 even more probable for the user. Theidentification of the one or regions can be based on, for example,location tracking data for other devices or based on information aboutmovement patterns in a community. The one or more regions may bereceived from a server system or generated by the medical treatmentdevice. Based on the physiological monitoring data and the detection ofa location in the one or more regions of elevated disease transmissionpotential, the medical treatment device may automatically provide one ormore therapies, e.g., by providing digital therapeutics interventions topromote behavior change or support specific functions (e.g., breathing,posture, exercise, etc.), dispensing a medication, etc.

The system can obtain monitoring data from individuals on an ongoing orcontinual basis using digital platforms, as well as obtain aggregateddata regarding communities and the effects of a disease. The system canuse these inputs to repeatedly generate updated predictions and also toupdate the predictive models. With incoming data streams showingreal-time behaviors of individuals, the system can provide rapid and insome cases real-time responses to changing conditions in a communitiesand in surrounding communities. The system can use predictive modelingto forecast future disease-related measures and trends, identify factorscontributing to those measures and trends, and recommend actions to moreeffectively improve a community's efforts to contain and eliminate adisease.

In addition, the predictions of the system can be used to personalizecare for individuals. For example, the system's predictions of futuredisease measures and trends in a community can be used to personalizedisease management actions taken for an individual in that community. Ifthe system predicts that of disease prevalence or infection rates willincrease in a community, for example, the system may change monitoringfor detection of (e.g., infection prediction for) the disease for anindividual, provide diagnostic testing for the disease for theindividual, or to begin or modify treatment of the disease for theindividual.

In addition, interventions and treatments can be provided across acommunity. For example, if a trip is planned for a family, a group offamilies, a business or other organization, etc., the system can detecta high risk of disease exposure and can take a disease management actionin response. For example, based on the intended destination, the systemcan ship medical monitoring or treatment devices in advance, canschedule doctor appointments or laboratory tests, and vaccination can bescheduled or vaccination status checked, etc. The system can acquireinformation about disease prevalence in different areas, such as fromgovernment sources or public health agencies, to detect locations whereoutbreaks of COVID-19 are present. The same techniques can be used forother diseases, such as yellow fever, malaria, polio, measles, or anyother infectious disease. As another example, if a field trip is plannedfor students, a vaccine for a disease can be distributed based on risklevels at a destination. As another example, a business may provide anemployee program, so that if some employees are traveling, treatment orvaccines are provided for the office or business as a whole, to reducesusceptibility to a disease that travelers may acquire and then exposeothers to.

One of the significant advantages that the system can provide is topredictively localize hotspots of disease transmission risk withprecision. Machine learning models can be trained using rich data aboutgeography and behavior in different communities. The examples used fortraining can include data describing specific places, datacharacterizing occupancy and traffic over time, geographic relationshipssuch as map data, and so on. The examples can also include individualand community-level disease outcomes (e.g., infections,hospitalizations, deaths, etc.) and monitoring data, such as locationtracking data indicating locations that people visited and the times,durations, and activities of the visits. Contact tracing data, ifavailable, can also be used to identify when and where differentinteractions leading to disease transmission occurred. With this data,machine learning techniques can train a model to incorporaterelationships among travel patterns, location types, environmentalfactors, geographical factors, and other factors with diseasetransmission. For example, the model can learn to identify the relativerisk of transmission posed by different types of locations when combinedwith different community characteristics and behavior or travelpatterns. As a result, when the system assesses a community data setthat has a location with this combination of factors, the model canindicate the high risk transmission risk for the location.

This approach can provide significant advantages over existingapproaches. For example, conventional contact tracing is a useful toolthat can track the chain of transmission of a disease among individualswho interact with each other or who are located at the same place at thesame time. Conventional contact tracing for COVID-19 typically involvesidentifying individuals who have an positive diagnosis of COVID-19(e.g., actual disease cases) and their contacts (e.g., anyone who waswithin 6 feet from a positive case for at least 15 minutes starting 48hours before the person began to feel ill until they were isolated), andthen working with diagnosed individuals and their contacts to preventfurther spread of the virus.

However, conventional contact tracing has limitations. Adoption ofconventional contact tracing technology is often limited and takes timeto deploy. In addition, contract tracing to track close interaction ofindividual does not necessarily capture all of the routes oftransmission of COVID-19 and other diseases (e.g., inhaled droplets,contaminated surfaces, aerosolized particles, etc.). For example, twopeople that visit the same location may not be detected through typicalcontact tracing, for example, for being outside a threshold distance(e.g., 10 feet), for visiting the location at slightly different times(e.g., a few minutes apart), etc. This type of visit could still resultin disease transmission however, through a contaminated surface or othertransmission mode. Another limitation of traditional contact tracing isthe relatively high level of contact required before finding that twopeople are in contact (e.g., often proximity of roughly 10-20 feet orless). Another limitation is the technique is that individuals areusually required to be located in the same place at the same time toregister contact, for example, being at the same place for overlappingtime periods of some minimum duration. While these factors can beuseful, they can also miss events that can nevertheless cause diseaseexposure, such as individuals passing through a region at times close toeach other, but not actually entering close proximity to each other orbeing at the same location at the same time. For COVID-19, which isbelieved to be spread on surfaces and through the air, potentiallythrough respiratory droplets or aerosolized particles, more versatiletools can better detect events and conditions that may spread COVID-19but not be detected by traditional contact tracing that focuses oninstances of people being at the same location at the same time.

The techniques discussed below enable a variety of features includinggeofencing, contact tracing, and proximity tracing. These featuresenable better communication with individuals, including those who havebeen officially diagnosed with COVID-19 to identify all individuals withwhom they have had close contact (“close contacts”) during the timeperiod in which they may have been infectious. Communication can also beprovided with those who are identified to be in close contact officiallydiagnosed individuals, e.g., a family member, a friend, a person livingwith them, or a random engagement. Communication can also be provided tothose who are identified to visit the same area as another person withina set period of time, such as 16 hours, or any defined amount of time.Exposure scores can be adjusted or weighted based on the amount of timeelapsed between the visits of different people. Communication can beprovided for those who have experienced any high-risk related areas andtheir observations. Based on estimates of exposure, the system can referindividuals to appropriate testing services, treatment, and generalguidance measures such as self-isolation steps. The system can alsomonitor individuals to maintain awareness of COVID-19 exposure rates andcorresponding risk identifiers. The system can assist individuals withnotifications, alerts, and community-related news of new, updated tagsrepresenting exposure events or significant exposure risks.

The system allows for the varying influence of different levels ofproximity and timing to be reflected in an individual's risk level. Inaddition, the system can incorporate additional factors such aspopulation volume or traffic levels, business types or activity typesfor different locations, and so on. This provides a more accurateindication of the exposure level or exposure risk of an individual,which allows the system to provide earlier and more appropriatelycustomized disease management actions (e.g., disease preventionrecommendations, testing, treatment, etc.).

In some implementations, location tags representing disease exposureevents or risks can be based on symptom reports or infection likelihoodpredictions, in addition to or instead of disease testing results. Forexample, a location tag representing a visit of a person can be assigneda disease transmission score based on symptoms reported by a user,physiological measures tracked (e.g., with passively sensed data),detected changes in behavior, or other early signs of infections, beforethe individual obtains a positive diagnosis. A machine learning modelcan be used to determine an infection likelihood for the user, allowinga high-confidence indication whether a person is infected with COVID-19before traditional testing occurs. By using these machine learningpredictions or other data such as behavior data, physiological data, orsymptom self-reporting data to assign scores to location tags, thesystem can better detect the spread of COVID-19 and recommend measuresto slow the spread of the disease, even before testing is available orwidespread.

Another advantage of the system is the ability to use machine learningto determine the types of events and contacts that result in diseasetransmission, and use that information to improve the scoring oflocation tags, ultimately resulting in a more accurate system over time.Many conventional contact tracing systems use predefined measures ofcontact, such as a specific proximity distance and amount of overlap intime, which may not be appropriate for the many varied types ofinteractions that occur and the varied locations people visit. Thepresent system is more versatile, allowing individual tags (e.g.,representing visits or events by individuals) to have different sizesand shapes of geofence areas and for the location tags to havetransmission scores (e.g., indicating the intensity or risk of diseasetransmission) to be different and to vary over time (e.g., to diminishgradually following the exposure event). In addition, differentthresholds and definitions of contact to be used to be customized, forexample, for different communities, for different types of locations,and even for specific individual locations. For example, due todifferences in ventilation and other environmental factors, the amountof time that a virus remains viable may be different for a grocery storecompared to a department store or another location type. Similarly, thepercentage of people at a location who have been vaccinated for adisease and/or who have recovered from the disease (e.g., and thusacquired immunity) limits the risk of disease transmission occurring. Byanalysis of the location tracking data and disease outcomes and relateddata, the system can learn the parameters that reflect the transmissionenvironment of different activities, locations, and location types. Thisallows the system to adjust and customize scoring of exposure-taggedlocations and the areas and tag timing for those locations (e.g.,including patterns of exposure risk reduction to elapsed time or socialdistancing precautions), for better exposure estimates and risk measuresoverall.

In some implementations, a system provides a technology platform thatcan be used to assess risks of a disease for an individual and selecttreatment for an individual. The system can be personalized or tailoredto the individuals and can be used as an ongoing risk assessment tool.The platform can include a downloadable data package (e.g., softwaremodule or set of configuration data) for managing the disease, which canbe downloaded to and used by a user device such as a smartphone. Thedata package can instruct monitoring of a user, for example, to obtainphysiological measurements, sensor data, survey responses, contextualinformation, and other types of data. This data can be provided to aserver, and can be processed by the user device and/or the server. Withthe collected data, the system can estimate a user's level of exposureto the disease and a likelihood of infection of the user. The system canalso select additional actions to improve detection of the disease, suchas to change monitoring procedures (e.g., the types of data collected,the frequency of data collection, etc.) or to issue a physical testingkit to test for the disease. The system can also select and recommendtreatment for the disease, such as through medication, behavioralchanges, digital therapeutics, etc. In general, the system can predict avariety of items, such as a user's personal risk of contractingCOVID-19, the severity of disease effects that may be experienced if theuser were infected, a likelihood that the user is currently affected,and so on. The system can also take actions to prepare against newrisks, for example, by encouraging a user to change behavior, to changeor increase monitoring, or take other preventive or protective steps. Asa result, the system can protect users from changing risks of disease bypredicting or estimating those risks and recommending or carrying outactions to reduce them. These steps can prevent or at least reduce theincidence and severity of diseases, including viral outbreaks, forindividuals.

The system can provide earlier and more accurate detection of diseaseusing improved techniques for generating and updating personal baselinemeasures. The system can generate baseline measures for each of multipleaspects of a user's physiology and behavior. Many prior systems have noteffectively used the predictive value of a user's behavior changes todetect and manage disease. In many cases, as discussed below, behavioralchanges may be an early indicator of infection or a predictor of theseverity or future symptoms of a disease. Nevertheless, behavioralchanges can be subtle and the significance can vary for differentindividuals. Using repeated, ongoing data collection from mobile devicesand other data sources, the system can maintain accurate baselinemeasures that take into account the contexts and activities of eachindividual, updating the baseline measures on an ongoing basis overtime. In some cases, the baseline measures can include average valuesbased on data collected over a time period. The baseline measures canalso include more complex types of data, such as data indicatingpatterns and trends over time and classifications of those patterns andtrends.

The system can also use data about each individual's community toenhance estimates or predictions of disease exposure, likelihood ofinfection, and other items. For example, the system can collectinformation about where a user resides, works, and generally spendstime, as well as the types of locations the user visits and the specificvisits and travel of the user. Information about infection rates andother disease measures for the communities where a user has been areused to better predict exposure and likelihood of infection. The systemcan use contract tracing data in this process, e.g., data trackingproximity of a user to specific other people over time. Nevertheless, inaddition to or as an alternative to tracking contact with specificpeople, the system can use monitoring data indicating locations a userhas been, amounts of time spent in the locations, occupancy or trafficat the locations, and other data to estimate exposure.

The system can progressively adapt the level of monitoring and types ofinteractions with a user according to the user's predicted exposurelevel or likelihood of infection. For example, as a user's exposurelevel increases, the system can automatically increase the frequency andextent of monitoring by user devices, e.g., smartphone, smartwatch orother wearable device, and so on. The system can vary the types ofmeasurements made (e.g., types of sensor data collected) as well as thetypes of surveys, user prompts, or other user interactions that thesystem causes to be provided. The system can also vary the monitoringprocedures used for a user to reach a desired level of confidence in thepredictions.

The monitoring data and predictions of the system can be used to performmay different actions to manage a disease, such as to select a testingkit for the user to test for the disease, selecting a vaccine andadministration regimen for the vaccine, and selecting treatment (e.g.,selecting drugs and dosage, selecting medical devices and settings forthem, selecting digital therapeutics, etc.). These actions and othersrecommended by the system can be selected based on monitoring data,clinical data (e.g., electronic health records (EHR) or electronicmedical records (EMR)), community measures, and more.

One of the significant advantages provided by the present technology isthe monitoring of user behavior and the use of that behavior data indisease detection and the selection of disease treatments. In manycases, subtle changes in behavior can occur during the very early stagesof disease, well before a user recognizes a symptom or realizes that heor she may be affected by a disease. By detecting subtle changes in thetypes, frequency, and intensity of user behaviors, including changes incombinations of different behaviors at similar times and in sequences,the present technology can identify early signs and risk factors forinfection that other systems cannot. These types of signs are also moreaccurately detected by comparison of observed data with personalizeduser baseline measures. Further, the present technology allows earlierindicators of changing behavior to be verified and enhanced throughfollow-up interactions such as surveys and other interactions that canhelp pinpoint the causes or factors influencing behavior. Thecombination of real-time behavior change detection with real-timephysiological monitoring, with the two being correlated and trackedtogether allows for predictive modeling and detection of infection andother conditions much better than physiological monitoring alone.

In general, behavior can be tracked to allow detection of certain typesof signs, symptoms, effects of disease and of disease treatment,including for items that are not readily detectable with physiologicalsensing alone. User behavior can be inferred from passively sensed data,such as location tracking, movement tracking, device pose and deviceusage, sounds detected with a microphone (e.g., detecting a usercoughing), etc. The behavior data can include records of differentactivities and events, indications of discrete instances of behaviors,as well as patterns and trends. A baseline measure can be determined andstored, and also periodically updated, for a user for each of differenttypes of activities. The baseline measures can indicate, for example,the type, frequency, duration, intensity, time of day, location, thatthe behavior takes place. Also, whether done alone or with others, whothe activities are done with, how regular the activity is, etc. Amongthe many types of behaviors that can indicate signs of disease infectioninclude changes in physical exercise, travel, social behavior, sleep,diet, and work behavior. Detected changes in behavior can also cause thesystem to initiate real-time interactions with a user to obtain relevantcontext and further information. For example, a user's device may detectthat the user is spending a greater time or frequency in bathroom thanusual, and so may send a survey asking if the user is experiencing GIdistress, which is one of many potential COVID-19 symptoms.

The behavior data provides the system context to better interpretphysiological data. For example, the behavior data can indicate whethera body temperature measurement represents a measurement during high,medium, or low activity, or during rest or sleep. This allowsmeasurements made during different activities or contexts to be used andinterpreted appropriately. This allows a greater range of data pointsthan are typically used by prior systems. For example, the presentsystem makes available new categories of measures and evaluations, whichcan allow detection of data patterns and disease signs and symptoms thatwould not be available if measurement were restricted to a singlecontext, such as during sleep. For example, the system can evaluatechanges in a user's work behavior, changes in a user's exercise, changesin a user's sleep, and more as indicators of potential disease. Forexample, a user may have a headache but not an elevated temperature,heart rate, or other physiological parameter that is tracked. Due to theheadache, the user may change his behavior, such as going to bed early,skipping a workout, moving more slowly at work, or staying home fromwork. The system can infer from the behavior changes that the user isnot feeling well, and can then initiate further monitoring and userinteractions to better determine the cause of the changes.

Some prior systems use physiological measures and monitoring to predictinfection, but are limited to observations made under a limited set ofconditions, such as when the user is sleeping. The present technology isnot so limited, and indeed can benefit from observations during diverse,varied types of actions. Along with sensor measurements obtained duringdifferent types of activities, the system captures context data thatdescribes the users activities. This allows the system to categorizedifferent physiological measurements according to the activitiesrelating to those measurements. For example, rather than using onlyresting heart rate measures for infection prediction, the system canmeasure and use measures of heart rate while walking, while running,while standing, and even at different levels of intensity of thosedifferent activities. The system can also create a personalized userbaseline measures for heart rate in the different types of activities.As a result, the system can detect heart rate changes with respect tobaseline levels that may occur during certain activities, such as briskwalking, even though there may not be corresponding changes in heartrate during rest, such as during sleep.

In one general aspect, a method is performed by one or more computers,the method comprising: providing, by the one or more computers, a datapackage for managing a disease to a user device of a user, the selecteddata package being configured to cause the user device to acquire andreport monitoring data to the one or more computers over a network;receiving, by the one or more computers, monitoring data provided by theuser device as directed by the data package, the monitoring datacomprising (i) sensor data generated using one or more sensors and (ii)user input comprising responses of the user to one or more surveys,wherein the monitoring data indicates one or more physiologicalmeasurements for the user and one or more behaviors of the user;generating, by the one or more computers, one or more scores for theuser based on the monitoring data, the one or more scores comprising atleast one of (i) an exposure score indicating a level of exposure of theuser to the disease, (ii) a susceptibility score indicating a predictedmeasure of effects of the disease for the user if the user becameinfected with the disease, or (iii) a detection score indicating alikelihood that the user is currently infected with disease; based onthe one or more scores, selecting, by the one or more computers, adisease management action from among a plurality of disease managementactions, the selected disease management action comprising changing adisease detection or disease monitoring procedure for the user orchanging a disease treatment for the user; and providing, by the one ormore computers, data to the user device or another device to cause theselected disease management action to be recommended or carried out forthe user.

In some implementations, the disease is COVID-19.

In some implementations, the monitoring data indicates that the userpresents with one or more symptoms of COVID-19, the selected diseasemanagement action comprises delivering one or more digital therapeuticsinterventions that are selected, by the one or more computers, to incitea change in a behavior of the user to treat the one or more symptoms ofCOVID-19 indicated in the monitoring data, the one or more digitaltherapeutics interventions comprising adjusting at least one of aposture of the user, a manner of breathing of the user, or a level ofphysical exercise of the user; and providing the data to the user deviceor another device comprises providing data that causes the user deviceto deliver the one or more digital therapeutics interventions byinitiating at least one interaction with the user configured to incitethe change in the behavior of the user.

In some implementations, the method includes: determining, by the one ormore computers and based on the received monitoring data, a diagnosisfor the user indicating whether the user is infected with COVID-19, andproviding, by the one or more computers, data indicating the diagnosisfor presentation by the user device.

In some implementations, the one or more computers are one or morecomputers of a server system; the data package is provided to the userdevice over a communication network by the server system; and themonitoring data is received by the server system from the user deviceover the communication network. The selected disease management actioncomprises changing a disease detection or disease monitoring procedurefor the user. Providing the data causing the causing the selectedmanagement action to be recommended or carried out for the usercomprises providing data that changes a configuration of the user deviceor another device associated with the user to alter at least one of (i)a type of data collected or reported by the user device, or (ii) afrequency at which data is collected or reported by the user device.

In some implementations, providing the data causing the selectedmanagement action to be recommended or carried out for the usercomprises: providing data that causes one or more digital therapeuticinterventions designated for treatment of the disease to be provided tothe user through the user device.

In some implementations, the provided data is configured to alter atype, intensity, or frequency of digital therapeutic interventionsprovided for the user.

In some implementations, the plurality of disease management actionscomprises at least one of providing a vaccine for the disease, providinga testing kit for the disease, administering a drug to treat thedisease, or delivering digital therapeutics to manage the disease.

In some implementations, the one or more scores are determined using oneor more machine learning models.

In some implementations, the selected disease management action involvesa vaccine for the disease, and the method includes selecting avaccination option for the user from among a plurality of differentvaccination options.

In some implementations, the selected disease management action involvesa testing kit for the disease, and the method includes selecting atesting kit for the user from among a plurality of different testingkits.

In some implementations, the method includes determining a user baselinemeasure for one or more physiological attributes of the user and one ormore behaviors of the user, wherein the one or more scores are based onthe user baseline measures.

In another general aspect, a medical treatment device configured totreat COVID-19 for a user, the medical treatment device comprising: oneor more sensors configured to measure a physiological parameter for auser, the physiological parameter comprising at least one of bodytemperature, blood pressure, heart rate, respiration rate, oxygensaturation, or blood glucose level; an input interface configured toreceive user input; one or more output devices configured to provide oneor more therapies to treat COVID-19 or symptoms of COVID-19; wherein themedical treatment device is configured to determine, based on locationtracking data, that the medical treatment device was located in one ormore regions of elevated potential for disease transmission, the one ormore regions being determined through a process of: receiving monitoringdata for a community, the monitoring data being generated using mobiledevices of individuals in the community, the monitoring data comprisinglocation tracking data that indicates locations visited by theindividuals; accessing community data for the community that describescharacteristics of the community and a geographic region associated withthe community; accessing one or more predictive models that areconfigured to evaluate regions for potential for transmission of adisease based on behavior patterns of individuals in the community, theone or more predictive models being trained based on training datadescribing a plurality of different communities and behavior patternsand disease outcomes of individuals in the different communities overtime; generating, using the one or more predictive models, an indicationof one or more regions of elevated potential for disease transmissionbased on data, derived from the monitoring data for the community, thatis indicative of behavior patterns of the individuals in the community;and providing output indicating the one or more regions identified usingthe one or more predictive models; wherein the medical treatment deviceis configured to provide the one or more therapies to the user based on(i) physiological measurements for the user using the one or moresensors and (ii) determining that the medical treatment device waslocated in the one or more regions of elevated potential for diseasetransmission.

In another general aspect, a method performed by one or more computersincludes: receiving monitoring data for a community generated usingmobile devices of individuals in the community, the monitoring datacomprising location tracking data that indicates locations visited bythe individuals; accessing community data for the community thatdescribes characteristics of the community and a geographic regionassociated with the community; accessing one or more predictive modelsthat are configured to evaluate regions for potential for transmissionof a disease based on behavior patterns of individuals in the community,the one or more predictive models being trained based on training datadescribing a plurality of different communities and behavior patternsand disease outcomes of individuals in the different communities overtime; generating, using the one or more predictive models, an indicationof one or more regions of elevated potential for disease transmissionbased on data, derived from the monitoring data for the community, thatis indicative of behavior patterns of the individuals in the community;and providing, to one or more devices over a communication network,output indicating the one or more regions identified using the one ormore predictive models.

In some implementations, the disease is COVID-19.

In some implementations, the actions of the method are performed bysoftware-as-a-medical-device (SAMD) on the one or more computers that isconfigured to diagnose and/or treat COVID-19. The method furthercomprises: determining an infection likelihood metric for a user thatindicates a probability that the user has an active case of COVID-19,the infection likelihood metric being based at least in part on (i) adisease exposure score determined based on location tracking dataindicating that the user entered the one or more regions and (ii) sensordata indicating one or more physiological measurements for the user; andproviding the infection likelihood metric for the second user to adevice associated with the user or a device associated with a healthcareprovider for the user.

In some implementations, the method includes providing, to the seconduser, digital therapeutic interactions for a course of treatment forCOVID-19 that includes exercises to assess and strengthen respiratoryfunction of the second user, wherein the digital therapeuticinteractions comprise one or more computer-directed therapies thatrequire approval by the U.S. Food and Drug Administration for use totreat COVID-19.

In some implementations, the method includes determining, by the one ormore computers, that a particular individual visited one of the regionsindicated by the one or more predictive models to have an elevatedpotential for disease transmission. Based on the determination, the oneor more computers select a disease management action for the particularindividual, comprising at least one of: selecting, from among aplurality of COVID-19 testing kits, a testing kit for the particularindividual; selecting, from among a plurality of COVID-19 vaccineoptions, a vaccine for the particular individual; or selecting, fromamong a plurality of digital therapeutics interventions, a digitaltherapeutics intervention determined to reduce or limit one or moresymptoms of COVID-19; and recommending or carrying out the selecteddisease management action to the particular individual.

In some implementations, the method includes determining, by the one ormore computers, that a particular individual visited one of the regionsindicated by the one or more predictive models to have an elevatedpotential for disease transmission; generating, by the one or morecomputers, an infection likelihood score for the user indicating alikelihood that the user has contracted COVID-19, the infectionlikelihood score being based at least in part on output that a machinelearning model provided based on processing data indicatingphysiological measures or behavioral measures for the particularindividual; and based on the infection likelihood score, automaticallyrecommending a medication or digital therapeutic intervention classifiedas treating one or more aspects of COVID-19 infection.

In some implementations, the method includes initiating, by the one ormore computers, delivery of the recommended medication or digitaltherapeutic intervention classified as treating one or more aspects ofCOVID-19 infection.

In some implementations, the location tracking data for at least one ofthe mobile devices is based on at least one of GPS data from a GPSreceiver or detected wireless signals from a Wi-Fi access point,wireless beacon, another mobile device, or a cellular base station.

In some implementations, the collected data further comprises (i)physiological data indicating one or more physiological measurementsdetermined for the individuals using the mobile devices, and (ii) userinput data indicating survey responses of the individuals provided usingthe mobile devices.

In some implementations, the one or more predictive models comprise oneor more machine learning models that have been trained to providedisease transmission scores indicative of a disease transmissionpotential for locations, the one or more machine learning models beingtrained based on training data examples for different locations, eachtraining data example indicating one or more location characteristic sfor a location, one or more community disease measures for a communitythat includes the location, and one or more behavior measures forvisitors to the location. Generating the indication of one or moreregions of elevated potential for disease transmission comprises usingthe one or more machine learning models to determine a diseasetransmission score for each of multiple locations in the community, eachof the disease transmission scores being based on one or more locationcharacteristic s for the corresponding location, one or more communitydisease measures for the community of the corresponding location, andone or more behavior measures for visitors to the correspondinglocation.

In some implementations, generating the indication of one or moreregions of elevated potential for disease transmission comprises:identifying locations in community that have a corresponding diseasetransmission score that satisfies a threshold; and generating data thatdesignates the identified locations as regions of elevated potential fordisease transmission.

In some implementations, the one or more machine learning models aretrained based on data describing measures of disease transmission thatoccurred at the locations for different training data examples.

In some implementations, the one or more machine learning models aretrained based on data indicating community characteristics of thecommunities in which the locations of the training data examples arelocated. Each of the disease transmission scores is an output generatedby the one or more predictive models in response to receiving a set ofinput values for a location, the set of input values for a locationindicating: one or more location characteristics for the location, oneor more community characteristics for the community in which thelocation is located, one or more community disease measures for thecommunity in which the location is located, and one or more behaviormeasures for visitors to the location.

In some implementations, the one or more machine learning modelscomprise one or more artificial neural networks.

In some implementations, generating the indication of one or moreregions of elevated potential for disease transmission is based on theuser input data indicating at least one of locations visited by theindividuals or activities performed at the locations visited by theindividuals.

In some implementations, generating the indication of one or moreregions of elevated potential for disease transmission is based onmeasures of at least one of: traffic levels, determined based on thelocation tracking data, at different locations in the community;occupancy levels, determined based on the location tracking data, atdifferent locations in the community; durations, determined based on thelocation tracking data, of visits to different locations in thecommunity; or movement patterns, determined based on the locationtracking data, of individuals during visits to locations in thecommunity.

In some implementations, the method includes obtaining data indicatingat least one of (i) disease prevention measures for the community or(ii) compliance with disease prevention measures at one or morelocations in the community. Generating the indication of one or moreregions of elevated potential for disease transmission is based on thedisease prevention measures or the compliance with disease preventionmeasures.

In some implementations, the community comprises a group of individualsresiding in a predetermined geographic area.

In some implementations, the predetermined geographic area is a county,city, or zip code.

In another general aspect, a medical treatment device is configured totreat COVID-19 for a user, the medical treatment device comprising: oneor more sensors configured to measure a physiological parameter for auser, the physiological parameter comprising at least one of bodytemperature, blood pressure, heart rate, respiration rate, oxygensaturation, or blood glucose level; an input interface configured toreceive user input; one or more output devices configured to provide oneor more therapies to treat COVID-19 or symptoms of COVID-19; wherein themedical treatment device is configured to determine, based on locationtracking data, that the medical treatment device was located in one ormore regions of elevated potential for disease transmission, the one ormore regions being determined through a process of: receiving locationtracking data indicating locations of user devices over time, each ofthe user devices being associated with a corresponding user; generatinglocation tags specifying visits of the user devices to differentlocations indicated by the location tracking data, each of the locationtags having corresponding data indicating a location of a visit, a timeof the visit, and a user device or user corresponding to the visit;assigning a geofence to each of the location tags that specifies ageofenced area corresponding to the location tag; assigning diseasetransmission scores to first location tags representing visits of asecond user, the disease transmission scores being based on dataindicating a disease status of the second user with respect to adisease; determining a disease exposure score for the user based ondetermining, using location tracking data for the user, that the userhas entered at least one of the geofenced areas corresponding to thefirst location tags, the disease exposure score being based on anaggregation of disease transmission scores for different location tagsfor which the user is determined to have entered the correspondinggeofenced area; and communicating a recommended disease managementaction to the user based on the determined disease exposure score forthe user; wherein the medical treatment device is configured to providethe one or more therapies to the user based on (i) physiologicalmeasurements for the user using the one or more sensors and (ii) theexposure score for the user.

In another general aspect, a method performed by one or more computersincludes: receiving, by the one or more computers, location trackingdata indicating locations of user devices over time, each of the userdevices being associated with a corresponding user; generating, by theone or more computers, location tags specifying visits of the userdevices to different locations indicated by the location tracking data,each of the location tags having corresponding data indicating alocation of a visit, a time of the visit, and a user device or usercorresponding to the visit; assigning, by the one or more computers, ageofence to each of the location tags that specifies a geofenced areacorresponding to the location tag; assigning, by the one or morecomputers, disease transmission scores to first location tagsrepresenting visits of a first user, the disease transmission scoresbeing based on data indicating a disease status of the first user withrespect to a disease; determining, by the one or more computers, adisease exposure score for a second user whose user device isdetermined, based on the location tracking data, to have entered atleast one of the geofenced areas corresponding to the first locationtags, the disease exposure score being based on an aggregation ofdisease transmission scores for different location tags for which theuser device of the second user is determined to have entered thecorresponding geofenced area; and communicating, by the one or morecomputers, a recommended disease management action to the second userbased on the determined disease exposure score for the second user.

In some implementations, the disease is COVID-19.

In some implementations, the actions of the method are performed bysoftware-as-a-medical-device (SAMD) on the one or more computers that isconfigured to diagnose and/or treat COVID-19. The method furthercomprises: determining an infection likelihood metric for the seconduser that indicates a probability that the second user has an activecase of COVID-19, the infection likelihood metric being based at leastin part on (i) the determined disease exposure score and (ii) sensordata indicating one or more physiological measurements for the seconduser; and providing the infection likelihood metric for the second userto a device associated with the second user or a device associated witha healthcare provider for the second user.

In some implementations, the method includes providing, to the seconduser, digital therapeutic interactions for a course of treatment forCOVID-19 that includes exercises to assess and strengthen respiratoryfunction of the second user, wherein the digital therapeuticinteractions comprise one or more computer-directed therapies thatrequire approval by the U.S. Food and Drug Administration for use totreat COVID-19.

In some implementations, the method includes, based on the diseaseexposure score for the second user, performing at least one of:adjusting, by the one or more computers, monitoring for COVID-19 for thesecond user; or initiating treatment for COVID-19 for the second user.

In some implementations, the method includes, based on the exposurescore for the second user: selecting, by the one or more computers, oneor more digital therapeutic interventions for the second user predictedto avoid or limit the severity of effects of the COVID-19; andproviding, by the one or more computers, data over a communicationnetwork to cause the user device of the second user to provide the oneor more digital therapeutic interventions to the second user.

In some implementations, the disease management action recommended tothe second user comprises at least one of: limiting travel; limitingsocial contact; wearing a face mask; taking a medication; taking a testfor the disease; or participating in a digital therapeutics program.

In some implementations, the method further comprises diagnosing, by theone or more computers, the second user with COVID-19 based at least inpart on the disease exposure score.

In some implementations, the method includes collecting, for each ofmultiple users, (i) physiological data indicating one or morephysiological measurements determined for the individuals using the userdevices, and (ii) user input data indicating survey responses of theindividuals provided using the user devices; and wherein at least one ofthe location tags or the disease transmission scores are determinedbased on the collected physiological data or the user input data.

In some implementations, the method includes informing a health agencyof at least one of: data describing instances of individuals enteringgeofenced areas corresponding to location tags generated based on visitsof users who are determined to have the disease or are predicted to havethe disease; or aggregate statistics for interactions with geofencedareas corresponding to location tags generated based on visits of userswho are determined to have the disease or are predicted to have thedisease.

In some implementations, the location tracking data for at least some ofthe user devices is based on global positioning system (GPS) datagenerated using a GPS receiver.

In some implementations, at least one location tag for a visit to alocation is generated in response to determining, based on the locationtracking data, that a user device (i) remained at the location for atleast a minimum amount of time or (ii) moved at the location in a mannerthat matches a predetermined movement profile.

In some implementations, for at least one of the location tags thatrepresents a visit by a mobile device of a user to a location, thecorresponding geofenced area has at least one of a size or shape basedon a path of movement of the mobile device during the visit to thelocation or an area traversed by the mobile device during the visit tothe location.

In some implementations, the disease status of the first user isdetermined based on a test result for the disease for the first user, adiagnosis for the first user with respect to the disease, or a healthrecord for the first user.

In some implementations, the disease status of the first user isdetermined based on a predicted likelihood of infection with the diseasefor the first user, wherein the predicted likelihood is determined usingone or more machine learning models and input to the one or more machinelearning models that is derived from at least one of physiologicalmonitoring data for the first user, behavioral monitoring data for thefirst user, or user inputs provided by the first user.

In some implementations, the location tracking data for at least one ofthe user devices is based on at least one of: GPS data from a GPSreceiver; detected wireless signals from one or more Wi-Fi accesspoints; detected wireless signals from one or more wireless beacon;detected wireless signals from one or more other user devices; anddetected wireless signals from one or more cellular base stations.

In some implementations, a location tag is generated for a location inresponse to determining, based on the location tracking data, that auser device: remained at the location for at least a minimum amount oftime; and/or moved at the location in a pattern characteristic of aparticular activity.

In some implementations, the disease status of the first user isdetermined based on at least one of: a test result for the disease forthe first user; an infection prediction score, determined using amachine learning model, that indicates a likelihood that the first userhas the disease; a diagnosis for the first user with respect to thedisease; a health record for the first user; one or more signs orsymptoms of the disease reported by the first user; or one or more signsor symptoms of the disease detected by a sensor of a device associatedwith the first user.

In some implementations, the method of claim 1, wherein determining thedisease transmission scores comprises: determining, for a particularlocation tag corresponding to a particular visit to a particularlocation by a particular user, a disease transmission score based on atleast one of: a prediction of infection likelihood for the particularuser; a duration of the particular visit at the particular location; anactivity performed during the particular visit; a location typeclassification for the particular location; a level of movement thatoccurred during the visit; one or more characteristics of the particularlocation; user-reported data indicating one or more conditions at theparticular location; and data indicating disease prevention measuresinstituted for a region that includes the particular location.

In some implementations, determining the disease transmission scorescomprises: determining, for a particular location tag corresponding to aparticular visit to a particular location by a particular user, adisease transmission score based on output of a machine learning modelprovided in response to receiving input data derived from datadescribing the particular visit and the particular location, wherein themachine learning model has been trained to provide an output indicativeof a risk of transmission of the disease based on training dataindicating visits of individuals to different locations and subsequentdisease transmission outcomes for the individuals.

In some implementations, determining the disease exposure score for thesecond user comprises determining the disease exposure score for thefirst user based on output of a machine learning model provided inresponse to receiving input data derived from (i) data describing thevisits of the second user and (ii) the disease transmission scores forthe location tags for which the user device of the second user isdetermined to have entered the corresponding geofenced area. The machinelearning model has been trained to provide an output indicative of arisk of contracting the disease based on training data indicating visitsof individuals to different locations and subsequent diseasetransmission outcomes for the individuals.

In some implementations, aggregating the disease transmission scorescomprises varying a level of influence of the disease transmissionscores based on an amount of time elapsed between (i) a time of thevisit represented by the corresponding location tag and (ii) a time ofthe visit to the geofenced area for the corresponding location tag bythe second user.

In some implementations, the disease status of the first user indicatesthat the first user has or is likely to have contracted the disease. Themethod includes: determining that the user device of the second user hasentered or is within a predetermined proximity of a particular geofencedarea corresponding to one of the first location tags; and in response todetermining that the user device of the second user has entered or iswithin a predetermined proximity of the particular geofenced area,providing a notification for presentation by the user device of thesecond user.

In some implementations, the exposure score for the second user isfurther based on population scores indicating of an occupancy or trafficlevel for different locations the user visited during a particular rangeof time.

In another general aspect, a medical treatment device configured totreat COVID-19 for a user, the medical treatment device comprising: oneor more sensors configured to measure a physiological parameter for auser, the physiological parameter comprising at least one of bodytemperature, blood pressure, heart rate, respiration rate, oxygensaturation, or blood glucose level; an input interface configured toreceive user input; one or more output devices configured to provide oneor more therapies to treat COVID-19 or symptoms of COVID-19; wherein themedical treatment device is configured to gather response data from theuser in response to a prompt for user input provided through the inputinterface, the prompt for user input being provided using contentdetermined through operations comprising: collecting user input dataprovided by one or more individuals in a community to one or more userdevices; accessing trigger data indicating one or more data collectiontriggers associated with monitoring of a disease, the data collectiontriggers respectively specifying one or more criteria and a type of datato be collected when the one or more criteria are satisfied; detecting aparticular data collection trigger indicated by the trigger data bydetermining that the one or more criteria for the particular datacollection trigger are satisfied; and in response to detecting theparticular data collection trigger: selecting content configured toprompt user input of the type of data associated with the detected datacollection trigger; selecting a set of individuals associated with thecommunity to receive the selected content; and communicating with userdevices associated with the individuals in the set to cause the userdevices to present the selected content that is configured to prompt foruser input regarding of the type of data associated with the detecteddata collection trigger; wherein the medical treatment device isconfigured to provide the one or more therapies to the user based on (i)physiological measurements for the user using the one or more sensorsand (ii) the response data provided by the user.

In some implementations, the one or more therapies involve providing oneor more interventions to change behavior of the user with respect torespiration, posture, exercise, sleep, or diet.

In another general aspect, a method performed by one or more computersincludes: collecting, by the one or more computers, user input dataprovided by one or more individuals in a community to one or more userdevices; accessing, by the one or more computers, trigger dataindicating one or more data collection triggers associated withmonitoring of a disease, the data collection triggers respectivelyspecifying one or more criteria and a type of data to be collected whenthe one or more criteria are satisfied; detecting, by the one or morecomputers, a particular data collection trigger indicated by the triggerdata by determining that the one or more criteria for the particulardata collection trigger are satisfied; and in response to detecting theparticular data collection trigger: selecting, by the one or morecomputers, content configured to prompt user input of the type of dataassociated with the detected data collection trigger; selecting, by theone or more computers, a set of individuals associated with thecommunity to receive the selected content; and communicating, by the oneor more computers, with user devices associated with the individuals inthe set to cause the user devices to present the selected content thatis configured to prompt for user input regarding of the type of dataassociated with the detected data collection trigger.

In some implementations, the disease is COVID-19.

In some implementations, the method comprises: receiving, by the one ormore computers, response data indicating responses to the prompts;generating, for a particular individual in the selected set ofindividuals, an infection likelihood score indicating a likelihood thatthe individual has contracted COVID-19, and providing, to the particularindividual or a healthcare provider for the particular individual, dataindicating a diagnosis whether the user the user has contracted COVID-19that is based on the infection likelihood score.

In some implementations, the method comprises: receiving, by the one ormore computers, response data indicating responses to the prompts;using, by the one or more computers, the response data to assesssuitability for one or more individuals of one or more digitaltherapeutics interventions classified as treating one or more aspects ofCOVID-19; and initiating, by the one or more computers, delivery of theone or more digital therapeutics interventions based on the suitabilityassessment.

In some implementations, the one or more criteria for the particulardata collection trigger comprise an evaluation of an output of a trainedmachine learning model. Detecting the particular data collection triggercomprises: generating an output produced the trained machine learningmodel in response to receiving a set of input data derived from at leastthe collected user input data; comparing the output with a threshold;and determining that the output satisfies the threshold.

In some implementations, the trained machine learning model is anartificial neural network.

In some implementations, the user input data comprises responses toprompts to the individuals presented by the user devices. The user inputdata indicates at least one of: a location visited by an individual; asign or symptom of a disease reported by an individual; a physiologicalattribute reported by an individual; or a result of a laboratory test ora test for a disease.

In some implementations, detecting the particular data collectiontrigger is further based on at least one of: a community disease measurefor a community; an electronic health record or one of the individualsin the community; a physiological measurement for individuals in thecommunity; or sensor data captured by the user device of one or moreindividuals in the community.

In some implementations, the data collection triggers are datacollection triggers to trigger collection of data indicative ofspreading of a disease, wherein at least one of the data collectiontriggers specifies criteria that includes detection of at least one of:a physiological attribute or physiological change for one or moreindividuals in the community; a sign or symptom of the disease for oneor more individuals in the community; a behavioral change for one ormore individuals in the community; a disease diagnosis for one or moreindividuals in the community; a positive disease test result for one ormore individuals in the community; or a community disease measure forthe community that satisfies a threshold.

In some implementations, at least one of the data collection triggersspecifies a type of data to be collected that includes at least one of:a physiological measurement; an indication a sign or symptom of thedisease for one or more individuals in the community; a behavioralchange for one or more individuals in the community; a disease diagnosisfor one or more individuals in the community; or a positive disease testresult for one or more individuals in the community

In some implementations, collecting user input data provided by one ormore individuals in the community comprises collecting user input dataregarding physiological attributes, behavior, and/or mental healththrough surveys that each include one or more prompts for user inputregarding physiological attributes, behavior, and/or mental health.

In some implementations, the surveys comprise ecological momentaryassessments (EMAs) configured to assess an individual's currentexperience, behavior, and/or mood at the time the EMA is provided and inthe current environment and context of the individual.

In some implementations, collecting user input data provided by one ormore individuals in the community comprises collecting user input datain response to a first survey provided to a first subset of members ofthe community. Selecting the set of individuals to receive the selectedcontent comprises selecting a second subset of members of the communityto receive a second survey, wherein the second subset is larger than thefirst subset, and wherein the second survey requests a different type ofdata than the first survey. Communicating with user devices to cause theuser devices to present the selected content comprises sending contentor instructions to the user devices to cause the user devices to presentthe second survey.

In some implementations, the trigger data specifies, for the particulardata collection trigger, a particular content item to provide to promptcollection of the type of data associated with the detected datacollection trigger. Selecting the content configured to prompt userinput of the type of data associated with the detected data collectiontrigger comprises selecting the particular content item specified by thetrigger data. The particular content item comprises a user interfaceelement, text, a question, a survey, an EMA, a user interface, a mediaitem, or an interactive element.

In some implementations, the one or more criteria for the datacollection triggers represent indicators of at least one of presence ofthe disease, spread of the disease, or risk factors for the disease. Thetypes of data respectively specified for the data collection triggerscomprise types of data used by the one or more computers to assessspread of the disease in the community or impact of the disease in thecommunity, the type of data comprising at least one of: physiologicaldata, comprising at least one of heart rate, blood pressure, respirationrate, oxygen saturation, lung capacity, or blood glucose level;behavioral data, comprising a measure for at least one of travel,exercise, sleep, work, recreation, diet, or device usage; or mentalhealth data, comprising an indicator for at least one of mood,responsiveness, anxiety level, or depression level.

In some implementations, selecting content configured to prompt userinput of the type of data associated with the detected data collectiontrigger comprises selecting two or more different content items that areeach configured to prompt user input of the same type of data associatedwith the detected data collection trigger. Causing the user devices topresent the selected content comprises causing the user devices to eachpresent one of the two or more different content items, wherein thespecific content item caused to be presented is determined based on atleast one of characteristics for a recipient, a user profile for therecipient, a prior interaction of the recipient, a medical history ofthe recipient, a disease status of the recipient with respect to thedisease, a vaccination status of the recipient with respect to thedisease, a sign or symptom of the disease indicated by the recipient, aphysiological measure for the recipient, a behavior of the recipient, ora mental health attribute for the recipient.

In another general aspect, a medical treatment device is configured totreat COVID-19 for a user, the medical treatment device comprising: oneor more sensors configured to measure a physiological parameter for auser, the physiological parameter comprising at least one of bodytemperature, blood pressure, heart rate, respiration rate, oxygensaturation, or blood glucose level; an input interface configured toreceive user input; one or more output devices configured to provide oneor more therapies to treat COVID-19 or symptoms of COVID-19 wherein themedical treatment device is configured to provide the one or moretherapies to the user based on physiological measurements for the userusing the one or more sensors; and wherein the medical treatment deviceis configured to assist the user to limit exposure to COVID-19 bypresenting disease exposure prevention options that are determined by:receiving user data indicating a prospective action of the user of themedical treatment device; detecting a trigger based on evaluation of thereceived user data; providing content configured to cause a mobiledevice associated with the user to present a prompt for user inputregarding the prospective action of the user; evaluating potentialfuture exposure of the user to COVID-19 based on response dataindicating a response to the prompt; selecting a disease exposureprevention option for the user that is predicted to reduce or avoidexposure of the user to COVID-19, wherein the disease exposureprevention option is selected or customized for the user based on atleast one of the user data or the response data; and providing contentconfigured to cause the medical treatment device associated with theuser to present the disease exposure prevention option.

In another general aspect, a method performed by one or more computersincludes: receiving, by the one or more computers, user data indicatinga prospective action of a user of a mobile device; detecting, by the oneor more computers, a trigger based on evaluation of the received userdata; providing, by the one or more computers, content configured tocause the mobile device associated with the user to present a prompt foruser input regarding the prospective action of the user; evaluating, bythe one or more computers, potential future exposure of the user to adisease based on response data indicating a response to the prompt;selecting, by the one or more computers, a disease exposure preventionoption for the user that is predicted to reduce or avoid exposure of theuser to the disease, wherein the disease exposure prevention option isselected or customized for the user based on at least one of the userdata or the response data; and providing, by the one or more computers,content configured to cause the mobile device associated with the userto present the disease exposure prevention option.

In some implementations, the disease is COVID-19.

In some implementations, the method includes, based on at least one ofthe received context data or the response data and physiological datafor the user, generating, by the one or more computers, a scoreindicative of a likelihood that the user has been contracted COVID-19,and initiating, by the one or more computers, (i) delivery to the userof a testing kit for COVID-19 to the user or (ii) delivery to the userof one or more digital therapeutic interventions classified as treatingone or more symptoms of COVID-19.

In some implementations, the received data comprises location trackingdata for a mobile device associated with the individual.

In some implementations, the received data comprises a user input to aprompt provided by a mobile device associated with the individual.

In some implementations, the context data indicates a location or pathof movement for the individual. The detected trigger is detected basedon determining that the user has entered or is approaching a particularlocation visited, within a predetermined period of time, by a personclassified as having COVID-19. The prompt requests information regardingan expectation of the individual regarding a current or upcoming visitto the particular location.

In some implementations, the prompt requests information regarding adestination, duration, or activity for the current or upcoming visit tothe particular location by the user.

In some implementations, detecting the trigger based on evaluation ofthe received context data comprises determining that an output generatedby a machine learning model based on the context data satisfies one ormore criteria associated with the trigger.

In some implementations, the user data is context data indicating acurrent context of the user. The prospective action is related to thecontext of the user;

In some implementations, the user data comprises calendar dataindicating an appointment for the user.

In some implementations, the user data comprises data indicating abehavior pattern or history of actions of the user.

In some implementations, detecting the trigger comprises determiningthat a likelihood of occurrence of the prospective action satisfies athreshold.

In some implementations, detecting the trigger comprises determiningthat a level of disease exposure risk for the prospective actionsatisfies a threshold.

In some implementations, detecting the trigger comprises determiningthat a context of the user has at least a predetermined minimum level ofsimilarity with a prior context of the user or of one or more otherusers that preceded performance of the prospective action.

In some implementations, evaluating potential future exposure of theuser to a disease based on response data indicating a response to theprompt comprises: determining (i) a disease transmission score for alocation where the prospective action is predicted to occur or (ii) adisease susceptibility score for the user that indicate a likelihood orseverity of disease symptoms if the user contracted the disease.

In some implementations, the response data indicates or confirms anintended activity of the user, wherein the disease prevention optioncomprises at least one of: wearing personal protective equipment,including at least one of a mask or gloves; maintaining a distancebetween the user and others; limiting a duration of the intendedactivity of the user; changing a location of the intended activity ofthe user; replacing the intended activity of the user with a lower-riskactivity; or avoiding the intended activity.

In some implementations, selecting the disease prevention optioncomprises selecting the disease prevention option based on output of amachine learning model generated in response to receiving dataindicating at least one of the intended activity, a location or locationtype for the intended activity, and collected data for the user.

In some implementations, selecting the disease prevention option thatstored mapping data specifies as corresponding to the activity type ofthe intended activity, a location type for a location of the intendedactivity, a disease transmission level for the location and/or intendedactivity, a user disease susceptibility measure for the user, and/oruser characteristics of the user. The mapping data associates differentdisease prevention options with different values of one or more ofactivity types, location types, disease transmission levels, userdisease susceptibility measures, and/or user characteristics.

In some implementations, the prompt for user input regarding theprospective action of the user comprises a prompt to: confirm whetherthe user intends to perform the prospective action or another action;indicate a destination or mode of travel of the user; or describeconditions for the prospective action, including at least one of alocation type for the prospective action, a location of the prospectiveaction, characteristics of a location for the prospective action, anumber of people at the location, an activity to be performed, orwhether disease prevention measures are used.

Other embodiments of these and other aspects include correspondingsystems, apparatus, and computer programs, configured to perform theactions of the methods, encoded on computer storage devices. A system ofone or more computers can be so configured by virtue of software,firmware, hardware, or a combination of them installed on the systemthat in operation cause the system to perform the actions. One or morecomputer programs can be so configured by virtue having instructionsthat, when executed by data processing apparatus, cause the apparatus toperform the actions.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features andadvantages of the invention will become apparent from the description,the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1, 3, and 5 are diagrams showing an example of a system fortesting, tracking, and managing COVID-19 and other infectious diseases.

FIG. 2 is a table illustrating information about different data packagesfor managing a disease.

FIG. 4 is a diagram showing examples of a decision process performed bythe system to manage a disease and customize interactions forindividuals.

FIG. 6 is another diagram illustrating the system.

FIG. 7 is a diagram showing processing of data regarding collected data.

FIG. 8 is a diagram showing signs and symptoms of disease and their usein disease detection, selecting testing kits, and adjusting treatment.

FIGS. 9A and 9B are user interfaces showing examples of user interfacesprovided to a user.

FIG. 10 shows examples of sensor data collected and digitalinterventions that can be provided using the system.

FIG. 11 shows an example of the system being used to make predictionsfor a community.

FIG. 12 shows an example of a user interface showing predictions for acommunity.

FIG. 13A shows an example of interactions between community-level datacollection and predictions and individual-level data collection andprediction.

FIG. 13B shows an example of providing notifications to individualsbased on community measures and predictions.

FIG. 14 shows an example of the system being used to use locationtracking and geofencing to determine disease exposure risks forindividuals and communities.

FIGS. 15A-15C and 16A-16D show examples of associating disease riskswith locations.

FIGS. 17 and 18 are examples of maps showing travel monitoring andlocation tracking data used by the system.

FIG. 19 is a flow diagram showing an example of a process for generatingdisease-related predictions for a community.

FIG. 20 is a flow diagram showing an example of a process for ofcollecting data and utilizing the data to improve disease management forindividuals and communities.

FIG. 21 is a table showing an example of trigger data.

FIG. 22 is a flow diagram showing an example of a process fordetermining disease exposure using location tags and geofenceinformation.

FIG. 23 is a flow diagram showing an example of a process for predictingfuture user actions and taking advance steps to limit or avoid diseaseexposure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

As discussed below, a system can collect and analyze data regardingphysiological, behavioral, and cognitive patterns indicative of theonset of COVID-19 and risk of COVID-19 infection. The system obtainsuser-reported data (e.g., survey responses, reported symptoms, etc.) andsensor measurements (e.g., heart rate, movement, location, etc.) andcompares these to baseline measurements for the user. From the varianceof collected data and differences from the a user's baseline levels, thesystem makes predictions of a user's risk of contracting COVID-19, theuser's likelihood of being currently infected with COVID-19, the currentdisease state, predicted disease progression, and so on. The system canuse these predictions to, for example, provide disease prevention andtreatment recommendations to individuals, provide decision support toclinicians, or determine the applicability of different testing kits forusers. While various examples herein are focused on COVID-19, the sametechniques can be used to detect, monitor, treat, and otherwise manageother diseases.

The system can generate and update measures of baseline characteristicsof users, and then use the baseline measures to match users toappropriate treatments. Among other actions, the system can matchindividuals to digital health interventions that are selected orpersonalized for each individual. This can include selecting oradjusting the digital therapeutics interventions for individuals toprovide the appropriate intensity of treatment, interaction, or support.

Digital therapeutics can be evidence-based therapeutic interventionsdriven by software programs (e.g., running at a client device, remoteserver system, or a combination of both) to prevent, manage, or treat amedical disorder or disease. The systems discussed herein can provide areal-time, interactive personalized medicine service that initiatescommunication and interaction with the user to deliver digitaltherapeutics. Among other effects, the digital therapeutics can involveinteractions, initiated by the system or by the user, that change theuser's behavior, for example, to reduce a symptom of COVID-19, to reducea risk level for COVID-19, to speed recovery from or to improve functionfollowing COVID-19 infection, and so on. These user behaviors andoutcomes that are affected are not merely behaviors interacting withdevices, but often represent changes in real-world aspects of the user'slife outside of human-device interaction. The digital therapeutics for aparticular patient, and the timing for delivery, are dynamicallyselected and updated using one or more digital therapeutics programsthat can address different aspects of a patient's care. Each program canhave a separate set of content, rules, assessments, and interventionsfor providing a customized, adaptive experience for a patient. Thesystem can operate in an “always-on” manner, frequently or continuallyassessing a data stream indicating the user's current status andcontext, and providing targeted interactions that are relevant to theuser's current or estimated future needs.

Digital therapeutics interventions can include various interactions,such as interactions through a smartphone or other user device. As a fewexamples, the system can inform a user of a health risk (e.g., such asan exposure risk for COVID-19, a susceptibility of the user to COVID-19,a likelihood of infection or diagnosis of infection with COVID-19,etc.), provide media, generate an interactive form such as a survey,provide a test or assessment, send a notification message, providerecommendations, provide content from a social media platform, provideinstructional activities or games, and so on. In some cases, the systemcan prompt a user to set, adjust, or view a goal, or challenge, remind,or inform the user about a goal. Similarly, the system may prompt a userto take an action, record a measurement from a device, provide contentfor a user to read or view, initiate a challenge for a user to changebehavior (or to perform a specific action or task). The system maycommunicate with family of a user, friends of a user, or othersregarding a user's goals or status, including with health serviceproviders. In general, interactions may involve visual output, audiooutput, haptic output, typed or touchscreen input, voice input, gestureinput, and other input/output modalities. The media provided as part ofthe interactions can include content such as text, videos, audiosegments, images, interactive instructional materials, messages (e.g.,indicating encouragement, reminders, etc.), games, and other content.

To make better predictions and provide more accurate diagnostic andtreatment recommendations, the system can provide interventions thatprompt users to complete an assessment at specific times during the dayor in response to specific situations or contexts. Examples includeecological momentary assessments (EMA). The system can also supportpassive ascertainment of changes in clinical status, in behavior, or inother aspects. The system can be configured to behavioral support, suchas self-management strategies, immediately following assessments ordetection of triggering conditions. The approaches to data collectionand treatment can be highly personalized. The system can tailor orpersonalize digital health interventions based on each individual'scharacteristics (e.g., race, gender, socio-economic status, etc.) fordisease prevention, presentation, management, and outcomes and thatultimately contribute to a more individualized approach to health care.

The COVID-19 pandemic creates an urgent need to protect individuals fromcoronavirus exposure, for example, by developing effective preventiontechniques, treatments, and vaccines. At the same time, it is criticalto allow society to return to normal as quickly as possible. Thecurrently available tools to contain the epidemic are limited, primarilysocial distancing and quarantine. There is a need for more precisedeployment of containment efforts only where needed, so that largersegments of the population can return to less restricted living. andreduce the risk of recurrence of devastating local outbreaks.

New digital health solutions can improve care, understanding of healthoutcomes, and risk factors related to the COVID-19 pandemic. Underservedpopulations can especially benefit, since they are oftendisproportionately affected by COVID-19 and often have limited access tohealthcare services. This is also important in its potential to broadenthe geographic understanding of factors related to exposure, spread, andcontainment.

The collection of large digital health datasets has potential privacyimplications. Systems that collect health-related data need adequateprivacy protections that enable personal health data to be used withoutunduly compromising civil liberties. Often, privacy concerns workagainst broad adoption of new technological data collection solutions.Ideally, a platform would address privacy concerns transparently, whilealso addressing the urgent need to interrupt the COVID-19 pandemic andfacilitate society returning to normal.

Improvement in the treatment of COVID-19 requires a high-qualityCOVID-19 research data set that can allow academic, public health, andtranslational researchers to make discoveries that might otherwise notbe possible from individual, isolated data sets. One area that thesedata sets can improve is in facilitating new research into digitalhealth technologies. The data generated by digital health technologieshas the potential to advance treatment and prevention of COVID-19 andother diseases. The same technologies can also improve public healthresponses and facilitate policies, organization, and other approachesthat could improve future epidemic and pandemic planning.

Many research questions regarding COVID-19 still remain. As a result,the techniques discussed herein can be adaptive and customizable totailor responses based on newly gathered data. An example, is the use ofmachine learning models and other technologies that can be continuallyupdated based on new research results and population-level observations.For example, as new treatments are applied and longer-term data becomesavailable for existing treatments, machine learning elements canincorporate the data to better weight. This can allow new observationsto feedback into the recommendations for disease prevention, monitoring,diagnosis, treatment, prognosis estimation, and long-term support. Manydifferent technologies are likely to be helpful in generatingcomprehensive, effective responses to the pandemic. Indeed, someresearch questions may only be able to be answered by integrating andanalyzing data generated by multiple different technologies andsolutions.

The techniques discussed herein provide technological solutions, such asa smartphone application, commercial wearable technology, computationalmodeling algorithms, approaches to data analysis and/or aggregation,etc. which can address one or more the following objectives:

-   -   tracing user contact with individuals diagnosed with or        suspected of having contracted COVID-19,    -   rapidly integrating commercial COVID-19 diagnostic test results,        patient-reported symptoms, wearable sensor data, electronic        health records, and/or other diverse    -   data sources with central or decentralized databases;    -   determining likelihood of user having undiagnosed COVID-19,    -   determining risk to a user of contracting COVID-19;    -   determining information that individuals, employers, government        agencies, and others can use to evaluate risk of allowing        individuals to return to normal work, travel, and public life        activities;    -   determining information that healthcare providers can use to        follow patients at home and intervene when or if physiological        decompensation occurs;    -   matching individuals to clinical trials for COVID-19;    -   ascertaining patterns of movement that influence exposure,        spread, and containment of COVID-19, and    -   providing strong privacy protections to allow integration,        analysis, and federation of existing datasets for the purpose of        COVID-19-related research.

The technology of the present application can be used to provide andevaluate digital health interventions (e.g., mobile health (“mhealth”),telemedicine and telehealth, health information technology (IT), andwearable devices) to address access, reach, delivery, effectiveness,scalability and sustainability of health assessments and interventionsfor secondary effects (e.g., behavioral health or self-management ofchronic conditions) that are utilized during and following the pandemic,particularly in populations who experience health disparities andvulnerable populations. To achieve this goal, the system can providedigital health interventions that address health disparities or focusparticularly on vulnerable populations. Digital health interventions canbe based on existing social and behavioral science theories as well asbest practice healthcare approaches. Interventions can take advantage ofthe unique functionality of mobile and wireless devices. The system canperform real-time data collection and feedback where appropriate.

The technology discussed herein can refine and test adaptiveinterventions and just-in-time interventions that can be ‘pushed out’via mobile technology based on information regarding the individual'scurrent state. For example, interventions can determine user state byprompting users to complete an assessment at specific times during theday or through passive ascertainment of changes in clinical status(e.g., ongoing passive monitoring by mobile devices, wearable devices,etc.). The system may immediately follow the determination of patientstate by providing behavioral support, such as self-managementstrategies. The technology discussed herein can use electronic healthrecord data in conjunction with digital health interventions to examinethe clinical epidemiology, service utilization, an/or response totreatment, within or across large systems responsible for health servicedelivery, in order to inform timing and targets for intervening.

The techniques in the present application can also be used to supportresearch to strengthen the healthcare response to Coronavirus Disease2019 (COVID-19) caused by the Severe Acute Respiratory SyndromeCoronavirus 2 (SARS-CoV-2) and future public health emergencies,including pandemics. Similarly, techniques can support research toevaluate various digital health interventions with respect to COVID-19or other diseases. For example, the techniques can be used to supportstudies designed to test if a proposed digital health interventionyields significant reductions in symptoms in individuals who exhibitclinically significant symptoms and/or functional impairment atbaseline. As another example, the techniques can be used to conductstudies that use software, devices, and systems that are interoperablewith existing infrastructure so that resulting data is interoperablewith relevant health information systems where applicable. In somecases, studies can collect data via well-established assessments andmeasures and leverage common data elements. Studies can testgeneralizable principles or approaches to using technology to improvethe accuracy and efficiency of assessment and the effectiveness andquality of intervention and service delivery. Studies may address knownchallenges with uptake and adherence or sustained use oftechnology-based approaches and attention to privacy and other safety orethical considerations associated with the use of technology forresearch and clinical purposes.

The techniques of the present application can identify and/or implementstrategies to increase the reach, access, engagement, efficiency,quality and sustainability of existing digital health interventions toaccommodate individuals seeking screening, prevention, self-management,wellness behaviors and treatment both during and following the pandemic,particularly in health disparate and vulnerable populations where priortrials may not have focused on these populations of interest. In somecases, the techniques can be used to design or carry out clinical trialsthat incorporate strategies to address the digital divide (e.g.,augmented digital interventions with non-digital components) as itrelates to vulnerable populations, including low-income communities,older adults, as well as their caregivers, who cannot easily use oraccess many digital interventions. Studies can include a conceptualframework that clearly identifies the target mechanisms and theempirical evidence linking the mechanisms to the study outcomes, plansfor assessing engagement of the target mechanisms, and analyticstrategies that will examine target engagement and associations withclinical benefit. In the case of multi-component interventions,strategies would address the target mechanisms corresponding to eachintervention component, as appropriate in the effectiveness context.

One of the advantages of the technology disclosed herein is the abilityto personalize risk measures, disease prevention, disease treatment, andother aspects of care. The technology can match individuals to digitalhealth interventions of appropriate intensity. When used to supportresearch, existing data (e.g., a database of prior collected data) canbe used to rapidly identify and enroll participants in research studies.Collected data can be used to select and apply algorithms for assigninginterventions to individuals. All of these strategies to increasetreatment fidelity to Evidence Based Practices (EBPs).

In the area of medical research, prospective trials can test theeffectiveness of algorithm-based intervention delivery, includingstepped-care approaches. As discussed further below, not only cantreatment actions be phased and adjusted for individuals, but preventionmeasures, testing techniques, and digital monitoring may also bedynamically adjusted, especially as the system predicts that a user'srisk of contracting COVID-19, risk of experiencing severe effects fromCOVID-19, or likelihood of being infected with COVID-19 change. Thetechniques discussed herein can use artificial intelligence, machinelearning, and natural language processing, whether through establishedmethods or new methodology.

The techniques discussed herein can include integration and analysis ofdata from multiple sources, including multiple digital health platformsor systems. When available, common data elements can be leveraged toidentify the long-term health impact and the cost effectiveness ofproviding digital health interventions at scale. In many cases, users ofdifferent types or with different attributes may have differentcompliance rates or usage rates for technology. Using the platformdiscussed herein, studies can test the differences in usage andeffectiveness of technology solutions among different groups orpopulations. For example, the platform can be used in studies thatinvestigate the influence of sex and gender on the use of digital healthinterventions for disease prevention, presentation, management, andpersonalized health care. Similarly, the platform can be used tofacilitate studies that are designed to test whether digitalinterventions can mitigate racial/ethnic and gender disparities inaccess, service utilization, and health outcomes.

Technology platforms can sometimes improve access to medical care. Forexample, steps can be taken to promote adherence to and sustained use ofdigitally assisted interventions, including studies that use existingdata to identify individual and intervention-level characteristics thatare associated with discontinuation vs sustained use, including COVID-19related stigma, and trials that test approaches to promoting sustaineduse and engagement. Some studies or data analysis can evaluate whetherchanges in service delivery (e.g., replacement or substitution offace-to-face services with digital health services) as a result of thepandemic will yield durable and sustainable system level changes inpractice that improve or maintain quality care. The social isolationmeasures of the COVID-19 pandemic provides an opportunity to evaluatewhether previously understudied or unknown services and care deliverypathways and ways to leverage these novel digital health pathways.Examples include ways to segment levels of care (e.g., crisis linesupport that could transfer to higher levels of telehealthpsychiatric/addiction care for more complex cases when in persontreatment is not feasible), bridge interventions, and therapy ortreatment sessions. For therapies that involve skill acquisition, aplatform can provide interactions to promote skill practice or skillacquisition between therapy sessions, and to facilitate the transitionback to face-to-face care, when needed.

FIG. 1 is a diagram showing an example of a system 100 for testing,tracking, and managing COVID-19 and/or other infectious diseases. Thesystem 100 includes a computer system 110, a communication network 108,data storage 120, a third-party server 130, and various user devices 104a-104 b and other devices. The computer system 110 can be a serversystem, located remotely from the user device 104 a-104 b of users. Thecomputer system 110 communicates with the other elements in the systemto request data, collect data, initiate interactions with users, andadaptively adjust monitoring and interaction with users. The computersystem 110 can also determine personalized predictions and diseasemanagement actions, such as selection of a testing kit, vaccine,medication, or digital therapeutic intervention.

The computer system 110 uses techniques of precision medicine andpersonalized medicine to tailor testing, detection, monitoring,diagnosis, treatment, and recovery assistance for COVID-19 and/or otherinfectious diseases for individuals. For example, the computer system110 is configured to monitor information about each of a variety ofusers in order to make personalized assessments for each user based oninformation collected for that user. The computer system 110 can performa variety of functions including generating estimates or predictions ofa user's exposure to a pathogen, detecting and diagnosing infection in auser, and assisting in disease prevention, treatment, and recovery for auser.

In FIG. 1, there is a first user 102 a, labelled “User 1,” who uses aphone or other user device 104 a. The user 102 a also has a wearabledevice 107 a, e.g., a smart watch, smart ring, or other instrumenteddevice, and a medical monitoring device 108 a. A second user 102 b isshown, who has a user device 104 b. In some implementations, the users102 a, 102 b can be patients who may be receiving care from a physicianor other medical care provider. In some implementations, the users 102a, 102 b may be participants in a research study, such as a clinicaltrial. In some implementations, the users 102 a, 102 b are simplyindividuals who are not associated with a medical care provider orresearch study, but are interested in tracking their exposure to adisease or in finding out quickly if they contract the disease. In someimplementations, the user device 104 a is a medical treatment devicethat may be capable of providing digital therapeutics, dispensing amedication, dispensing or injecting a vaccine dose, etc. For example,features of the user device 104 a or other user devices discussed hereinmay be incorporated into medical monitoring and treatment devices suchas glucometers, insulin pumps, blood pressure cuffs, and so on.

In stage (A), the computer system 110 selects and provides a datapackage to one or more user devices. The data package can initiateinteractions with users and collection of sensor data by the devices.Examples of sensors that can be used include accelerometers, lightsensors, cameras, microphones, inertial measurement units (I MUs), GPSreceivers, and compass sensors. Other devices can include other sensors,such as pulse or heart rate sensors, EKG sensors, and so on. The datapackage can be configured to cause a device that receives the datapackage to initiate monitoring and reporting of data to a server, suchas the computer system 110, over the network 108. For example, a datapackage can cause a device to collection sensor data on a periodic basisor in response to the occurrence of certain conditions or triggers. Thedata package can also cause a user device that receives the data packageto initiate interactions with a user, such as to present a survey orelectronic form for receiving information from a user. Information aboutuser interactions, e.g., user input provided in response to a survey, isthen reported to a server, such as the computer system 110. In someimplementations, a module 112 includes software or configuration data todirect data collection and reporting and interactions with the user. Inaddition, or as an alternative, the module 112 can include software orconfiguration data that cause a user device to receive and carry outdata collection, reporting, and user interactions as directed by one ormore servers, such as the computer system 110, which may specifydifferent user device behavior and different user interactions atdifferent times.

In FIG. 1, the computer system 110 stores multiple data packages, shownas modules 112. These modules can include executable software,configuration settings, instructions (e.g., to an application oroperating system), media content, and/or other elements. A module 112can include rules, models, logic, or other elements that enable localprocessing and analysis of collected data and condition initiation ofactions based on the results of data analysis. In some implementations,the modules 112 are applications that can be installed on and run on auser device. In some implementations, the modules 112 enhance or changethe functionality of an existing application. An application candownload and install a module 112 to add functionality to or makeavailable functionality of an application previously installed on a userdevice. This can customize or tailor the behavior of an application. Insome implementations, the modules 112 include sets of configuration datain a format that an application can read and apply to carry outmonitoring, data collection and reporting, user interactions, and so onthat are specified by the module. The modules 112 can be modules asdiscussed in U.S. Pat. No. 9,858,063, issued on Jan. 2, 2018 and titled“Publishing Customized Application Modules,” which is incorporatedherein by reference. The modules 112 can be form data packages asdiscussed in U.S. Pat. No. 9,928,230, issued on Mar. 27, 2018 and titled“Variable and Dynamic Adjustments to Electronic Forms,” which isincorporated herein by reference.

The computer system 110 stores a variety of modules 112 that areconfigured to assist in data collection and monitoring to manage adisease, e.g., to prevent a disease, detect a disease (e.g., determineexposure or infection), treat a disease, and/or support recovery from adisease. In some implementations, the modules 112, can be consideredsoftware as a medical device (SaMD), as it can be used to diagnose andtreat a disease such as COVID-19. Indeed, in some implementations, themodules 112 (potentially in combination with another application and/ordata and instructions from the computer system 110) can providetreatment directly to the user 102 a through the user device 104 a,through digital therapeutics interventions involving interactions withthe user 102 a that can, for example, incite behavior change in theuser. Similarly, the software of the computer system 110 can beconsidered to be SaMD as it is designed to treat and/or diagnosedisease, as well as to drive clinical management and inform clinicalmanagement. In some implementations, the interactions that the modules112 cause can include digital therapeutics interventions, customizediagnosis and diagnostics, and recommendations for prevention,treatment, risk reduction for specific diseases. The selection of whichmodule 112 to provide can be personalized for individual users. Inaddition, the types of interactions that the module 112 causes and themanner of collecting data or providing treatment to a user can also becustomized for that user.

The modules 112 can be applications that are executed on the userdevice, for example, different mobile device applications. In someimplementations, the modules 112 are packages that enhance or expandanother application already installed on a user device. For example, amodule 112 may include data causing a user device to perform variousdata collection and reporting functions. These can include providingsurveys or other user interfaces for display, collecting responses, andproviding them back to the computer system 110. As another example, amodule 112 they include code, configuration data, or other elements thatcause a user device to monitor certain types of sensor outputs, processthe data, and report results to the computer system 110.

The modules 112 may specify various aspects of data collection andanalysis. These can include types of sensors to monitor, frequency ofmonitoring, precision data collected, format of data collected, triggersfor conditions that cause sensor data collection or alter sensor datacollection, and so on. The module 112 can include rules that areexecuted by the user device to selectively perform monitoring actions,for example, to vary the types of data collected in manner of theircollection using sensors and user prompts based on conditions or contextdetected or other factors.

The computer system 110 can provide the modules 112 to user devices toenable the user devices to collect and report data used by the computersystem 110 and to interact with users. The modules 112 may be providedin response to a request from a device 104 a, such as a request sent inresponse to a selection of the module 112 by the user 102 a from a userinterface of an application, from a selection of a link in a webpage, orother action. Because multiple modules 112 are available, the computersystem 110 select an appropriate module from among the set of modules112 available. For example, there may be multiple modules related toCOVID-19, with different modules being tailored for different types ofusers, users with different demographic characteristics, differentlocations of residence, different professions, and so on. As anotherexample, different modules 112 may be available for different aspects ofmanaging COVID-19, with different modules 112 respectively tailored fortreating active infections, assisting with recovery post infection,detecting and preventing infection, and so on.

Each module 112 can have associated data specifies the applicability ofthe module 112, e.g., metadata or a profile indicating sets of usercharacteristics that make that module 112 applicable or inapplicable toa user. The computer system 110 uses an initial set of information abouta user, which may be provided by a user through a form, or may beprovided by a clinician or from EHR/EMR, which may be stored in a userprofile for the user based on prior interactions. The computer system110 compares the characteristics of the user with user characteristicsof the various modules 112 and selects one or more modules determined tobe appropriate for the user. Additional information about the modules112 and selection of modules is discussed with respect to FIG. 2.

In the example of FIG. 1, the computer system 110 users informationabout first user 102 a obtained from medical records 131 to determinethat a first module 112 a is appropriate for the user 102 a, based onthe user's age, current health conditions, and other factors. For asecond user 102 b, the computer system 110 selects and provides adifferent module 112 b based on the characteristics of the user 102 b.

In stage (B), the user devices 104 a-104 b receive and process thereceived modules 112 a, 112 b. These modules initiate interactions withthe users and also cause the user devices 104 a, 104 b to initiatecollection of sensor data according to the parameters and code specifiedin the modules 112 a, 112 b. For example, the module 112 a may cause theuser device 104 a to begin periodically measuring and recordinglocation, physical activity or movement of the device 104 a, and otherparameters. The module until they may also cause the user device 104 ato measure other parameters. The module until they may also cause theuser device 104 a to collect other data from other devices, such aswearable device 107A and a medical monitoring device 108 a. Examples ofmedical monitoring devices include blood pressure cuffs, weight scales,glucometers, pulse oximeters, and so on. In some cases, the device 108 amay be a medical treatment device. Examples of wearable devices includesmart watches, smart rings, activity trackers (e.g., FITBIT devices),wristbands, necklaces, clothing, or other devices including sensors anddata transmission capabilities. The user device 104 a can connect withmedical monitoring devices and wearable devices and other devices, e.g.,entertainment systems, navigation systems, vehicles, home automationsystems, smart appliances, and so on, to collect data as directed by adownloaded module 112.

In stage (C), the mobile devices 104 a, 104 b collect data and reportdata to the computer system, as directed by the previously receivedmodules 112 a, 112 b. The data is sent over the network 108 and receivedby the computer system 110.

In the stage (D), the computer system 110 collects and processes thereported data. The computer system 110 can store each of the data itemsreported, and can use the data to generate personalized baselinemeasures for each user. Typically, a personalized baseline is determinedfor each of multiple measures, which can include behavioral measures,physiological measures, and more.

The personal baseline information can be based on information gatheredovertime. Such as the previous three days, the previous week, theprevious two weeks and so on. In some cases, baseline measures aredetermined and stored for each of multiple different prior periods. Thebaseline can be specified in different ways. As an example, the baselinevalue for a parameter may be an average value over a period of time.Measures of variability over the period of time can be included (e.g.,indicating range of values, standard deviation, etc.). As anotherexample, the baseline can indicate patterns or trends. For example, auser's typical pattern of physical activity may show different levels ofactivity for different days of the week, and the baseline measures canindicate this pattern.

Personal baseline measures can be determined and stored for variousdifferent aspects of health and behavior. For example, in the FIG. 1,there are baseline parameters for physical activity level, a coughingscore, a sleep score, body temperature, a respiration rate, and aresting heart rate, and potentially others. The types of parameters thatare measured and for which baseline measures are determined and varybased on the level of monitoring used for user. Different modules 112can you collect different information, and so they allow different setsof baseline measures to be determined. As discussed further below, thelevel of monitoring used for a different user can vary based on manyfactors, such as a user's personal level of exposure to the disease,infection rates in the community where do user lives, or a user'spersonal susceptibility or risk for the disease (e.g., based on age,physical fitness, existing health conditions, and so on).

The example of FIG. 1 illustrates the importance of baseline data andcontextual data in making accurate prediction and diagnosis of COVID-19and other diseases. A user's personal baseline can have a significantimpact on the way monitored values influence predictions and decisionsof the system. In the example, User 1 and User 2 have different personalbaseline measures, which results in different decisions by the system110. FIG. 1 shows a table 160 with data for User 1, and a table 170 withdata for User 2. Both users have a current physical activity level scoreof 2, representing activity over the previous day. For User 1, thisrepresents a noticeable decrease compared to the baseline level, whilethis is consistent with the baseline for User 2. This is one examplewhere the decrease in activity for User 2 may be an indicator ofillness, where the same level of activity for user to does not indicateincreased likelihood of illness for other users.

As another example, for the coughing score, both users have a level ofone, indicating that some rare coughing was detected. For User 1, thisis a change from the baseline behavior, and so it indicates an increasedlikelihood of illness, where the same score for user two does notbecause it is consistent with the user's baseline. Similarly, with thesleep score, the User 1 data shows a decrease in the quality and orquantity of sleep, which may be an indicator of disease. For the bodytemperature measurements, the current temperature of 99.2 for User 1 iselevated, but the difference is very slight given that users baselinetemperature of 99.0. By contrast, User 2 has a lower temperature thanUser 1, but shows a larger increase compared to the User 2 baseline, andas a result, the computer system 110 may determine that this is a moresignificant indicator of possible disease than the temperature ofUser 1. For respiration rate, the user to respiration rate is higher at16 breaths per minute then the User 1 respiration rate of 14 breaths perminute. However, the User 1's lower baseline shows that 14 breaths perminute is significantly higher than the User 1 baseline, and so this maybe an indicator a disease for User 1, where it would not be for User 2.Finally the current resting heart rate values are the same for bothusers at 75 bpm, but this is an increase for User 1 where it is not foruser two. As a result, the resting heart rate may be interpreted by thecomputer system 110 to increase the likelihood of disease for User 1,where it does not for User 2.

In general, the computer system 110 can generate and store personalbaseline measures for a variety of different types of measures orparameters, including physiological measurements, behaviors, cognitiveattributes, user performance levels, and mood and mental health.Physiological measurements include, e.g., heart rate, respiration rate,blood pressure, blood glucose level, oxygen saturation (e.g., SpO2),lung capacity or VO2 max, body temperature. A few examples of behaviorthat can be tracked, and for which a baseline can be provided includesleep, diet, exercise, travel, rest, and work. As noted above, thepersonal baseline measures can indicate scores or values for differentparameters in each of the types or categories noted above. The baselinedata can be provided in different ways, such as values or scores thatare aggregated or synthesized over time (e.g., a score that is averagedor synthesized from multiple measurements or data collection events atdifferent times. The baseline measures can include data specifying arange of values over time (e.g., the resting heart rate range over thelast day, over the last 7 days, etc.) and/or statistical measures (e.g.,standard deviation, variance, etc.). As another example, a time seriesof recent measurements for each of one or more parameters can beprovided (e.g., the resting heart rate for each of the last seven days).In addition, or as an alternative, the personal baseline measures maydescribe or indicate patterns or trends that are identified or derivedfrom collected data, e.g., a starting value along with an amount ofchange and direction of change, a curve or equation describing thepatterns, a classification of the pattern selected from a group ofdifferent classifications (e.g., stable, fluctuating in a range, slightincrease, moderate increase, large increase, slight decrease, etc.).Classification of different baseline conditions or patterns can be doneusing a trained machine learning model to determine the most likelyclassification, or by comparing characteristics of data patterns toreferences (e.g., thresholds, ranges, etc.) corresponding to differentclassifications. Personal baseline measures can also measure elementssuch as the frequency and timing of certain types of events oractivities, measures of cognitive or physical performance duringactivities prompted or guided by the user device 104 a, and so on.

In stage (E), the modules 112 facilitate interaction with users andcollection of user reporting data. This can include generating promptsor surveys for the user, whether generated locally by a user devicebased on an application and or module 112, provided by the computersystem 110 over the network 108, or generated through a combination ofclient and server actions. For example, the computer system 110 and/orthe module 112 can cause an ecological momentary assessment (EMA), e.g.,a survey or other interaction that assesses an individual's currentexperience, behavior, and mood as they occur in real time and in theirnatural environment.

In some implementations, the computer system 110 evaluates the qualityof data received and may initiate adjustments to the monitoring processto improve the completeness or quality of data collected for a user. Asanother example, the computer system 110 can evaluate the incomingstream of data for each user, and compare it to evaluation standards. Ifthe data is not of sufficient quality according to the standards, forexample, if frequency of measurement, consistency of reporting,reliability of the data, and so on are not meeting the standards, thecomputer system 110 may communicate with devices to change theircollection and reporting settings to correct the problem.

As the computer system 110 receives additional information, the computersystem 110 can also analyze the received data and determine whetheranother module 112 would be appropriate for the user, e.g., tosupplement or replace a module already obtained.

FIG. 2 illustrates various examples of different modules 112 that can beprovided to users. As illustrated, different modules 112 can be providedfor different purposes (e.g., detection of infections, treatment ofinfections, support in recovery after the infection has subsided, etc.).There can also be different versions of the modules for different userpopulations, e.g., different modules 112 for groups of users havingdifferent risks of harm if they contract COVID-19.

The different modules 112 can provide different types of monitoring,suited to the expected needs and risks of different users. For example,the first module tracks a certain set of physiological data andbehavioral data for a user, and also provides a set of surveys. Thesecond module is intended for higher-risk users, such as those aged 60year and older, and it includes more extensive monitoring and moresurveys. The second module monitors the same types of data as the firstmodule, but also adds several additional types of data to track. It alsoprovides a more extensive set of surveys to provide.

The appropriate module 112 for a user can be selected in different ways.In some implementations, a physician or other medical care provider mayrecommend or prescribe a module 112 for a user. As another example, oneor more of the modules 112 may be made available to users, and a usermay select a particular module to download. For example, a web page orapplication may provide a user interface at the user device 104 a thatprovides a list or gallery of available modules 112, and a user caninteract with the user interface elements to select a module 112 todownload and use.

The characteristics of a user and medical history of a user can be usedto determine which module 112 to provide for a given user. For example,each module 112 can be associated with a profile or set of criteriaspecifying the conditions or set of user characteristics for which themodule 112 is indicated for use. For example, among modules configuredto detect, monitor, and/or treat COVID-19, the criteria for one module112 may specify that it is applicable for users in a certain age rangethat do not have chronic conditions, the criteria for another module 112may specify that it is applicable for users in a different age rangethat do not have chronic conditions, the criteria for another module 112may specify that is applicable for users that have diabetes and are in acertain age range, and so on. Different modules 112 can be provided fordifferent combinations of user attributes and health conditions, such asage, sex, height, weight, health conditions, physiological attributes,behavioral attributes, geographical region, and so on.

In some cases, in addition to or instead of having different modules 112for different user types or user attributes, a module 112 (e.g., ageneral COVID-19 monitoring and treatment module) can be provided, andthe monitoring and interactions that the module 112 directs can bepersonalized based on user attributes, health history, and other userdata. Thus, if health history for a user indicates diabetes, high bloodpressure, cancer, or other conditions that affect the need formonitoring and treatment, the device that receives the module 112 and/orthe computer system 110 can use information about the user topersonalize the actions performed based on the module 112. The moduleitself 112 can provide different profiles or sets of configuration datato address different combinations of user health factors or userattributes, and/or the computer system 110 can provide communications orotherwise cause interactions through the module 112 that arepersonalized for the user's characteristics, history, and medicalcondition. Further, any of the modules 112 discussed herein can beadaptive to change monitoring and treatment to suit the data collectedand the user's behavior and changes in physiological attributes overtime. This can be done taking into account the personal baselinemeasures, which are updated as new information is received. Theadaptation in monitoring and treatment can also be responsive to changesin predictive measures for the user, such as user scores for diseaseexposure level, disease susceptibility, likelihood of infection, and soon as discussed below.

Modules 112 can be provided for different purposes. For example, somemodules 112 may be used for clinical monitoring and treatment, whileothers may be for health research studies, others for community healthmonitoring, etc. In some cases, a different module 112 can be providedfor different health research studies, with each research study module112 directing data collection, monitoring, treatment, and reporting ofdata according to the study's study protocol, to provide collected datato the appropriate researchers. Of course, a single module 112 maycombine functions to serve one or more of these different uses (e.g.,clinical support, research, community monitoring, etc.).

The modules 112 can be configured to behave differently for users indifferent locations, or different versions of the modules 112 can beprovided for different geographic regions. For example, privacyrequirements are different in different countries, social distancing andother preventative measures vary from location to location, and socialand cultural norms vary from location to location. The modules 112 canthus be configured to customize their actions for the location in whicha user is located.

FIG. 3 shows additional operations of the computer system 110 and/oruser device 104 a to process data obtained through the monitoringdiscussed in FIG. 1. Various operations in FIG. 3 will be discussed withrespect to the computer system 110, which can be a remote server system.Nevertheless, some or all of the operations can be done by the userdevice 104 a. In general, FIG. 3 shows various stages (F) to (G) inwhich the system assesses incoming data for a user 102 a and then usespredictive models 230 (e.g., trained machine learning models) to makepredictions for a user regarding the user's exposure, susceptibility,and/or infection likelihood for COVID-19. With those predictions, thesystem selects personalized actions to improve monitoring or treatmentfor the user 102 a, such as to adjust the type and intensity ofmonitoring according to the risks for the user, to change configurationdata and monitoring procedures of the user device 102 a, to recommendadditional monitoring devices, to recommend laboratory tests, and so on.As discussed further below with respect to FIG. 5, the user scores 240and other predictions can be used to select other disease managementactions for a user, such as selecting and determining to provide atesting kit, a vaccine, digital therapeutics, pharmaceuticals, medicaldevices and medical device settings, and other therapies.

The predictive models 230 (and other predictive models discussed herein,including models 530 a-530 d and model 612 discussed below) can bemachine learning models, for example, one or more neural networks orclassifiers. Other types of models that may be used include supportvector machines, regression models, reinforcement learning models,clustering models, decision trees, random forest models, geneticalgorithms, Bayesian models, and Gaussian mixture models. Differenttypes of models can be used together as an ensemble or for makingdifferent types of predictions. Other types of models can be used, evenif they are not of the machine learning type. For example, statisticalmodels and rule-based models can be used. The computer system 110 cananalyze the examples data about many users in a database to determinerelationships between data factors and COVID-19 disease outcomes andconditions, either through explicit analysis or through machine learningtraining, e.g., so that a model implicitly learns the predictive valueof different data items on, for example, disease exposure, diseasesusceptibility, infection likelihood, etc. Training can incrementally oriteratively update the values of parameters in the models 230 to learnthe impact of different factors on predicted outputs. In the case ofneural networks, backpropagation can be used to alter neural networkweights for neurons at various layers of a neural network model.Training can be done using stochastic gradient descent or other trainingalgorithms.

In stage (F), the computer system 110 uses collected data about the user102 a to determine if any of various markers are present. The computersystem 110 can store data that specifies marker definitions 208 thatdefine different markers. The markers can represent different dataelements that indicate or predict disease-related factors. A marker maybe an indicator of a sign or symptom of a disease, but need not be. Forexample, some markers may represent factors that increase risk ofcontracting a disease or risk of increased severity of disease, even ifpresence of the marker on its own does not indicate any actualinfection. The markers can be defined as one or more measurements orobservations of physiological measures, behavioral measures, cognitivemeasures, mental health or mood measures, or a combination thereof.Markers can be based in part on long-term user attributes (e.g., age,sex, height, weight, chronic conditions, medical history, etc.) and/orrecent or even real-time measures (e.g., monitoring data over the lasthour, day, week, etc.). Disease effects and outcomes can be differentfor different people and different situations. As a result, a value(e.g., resting respiration rate over 70 breaths per minute) may serve asa marker for increased disease severity risk in one type of person(e.g., someone with high blood pressure) but not for another category ofpeople (e.g., people without high blood pressure) The marker definitiondata 208 may include criteria associated with each marker to define whenthe marker is considered to be present. These criteria may includecertain values for measures (e.g., a value above a threshold, a value ina predetermined range, a value with a certain type or level of changewith respect to a corresponding personal baseline measure, etc.). Inaddition, or as an alternative, a marker may be defined as being presentwhen a combination of measurements or conditions occur together inmonitoring data, and/or when a pattern of monitoring data over time(e.g., when one or more measures changes in a certain way over a seriesof measurements).

The computer system 110 or another computer system can examine variousdata sources to identify data values, patterns, and combinations of themthat are predictive of or indicative of, for example, infection,susceptibility, or exposure to a disease. For example, the markers maybe defined to represent different data patterns the indicate thepresence of one or more symptoms of COVID-19. As another example,markers may be defined to represent data patterns that indicate levelsof exposure risk. For example, the types of locations a person visits,the amount of travel for different locations or use the visits, thelength of time or user visits, and so on indicate risk factors forexposure to COVID-19. The computer system 110 can use statisticalanalysis and/or machine learning techniques to analyze collectedmonitoring data for various other users to define what factors (e.g.,what types of data, values, combinations, and patterns) are correlatedto different outcomes (e.g., disease infection, disease severity,specific disease symptoms, the need for specific interventions such ashospitalization or ventilator support, etc.). The factors that are mostpredictive of outcomes of interest are then recorded and saved asdefinitions for markers. In some implementations, the markers can thenbe used as machine learning features provided to trained models to makepredictions for users.

A comparison module 210 compares monitoring data for the user 102 a anduser profile data (e.g., user attributes, medical history, etc.) for theuser 102 a to the criteria specified in the marker definitions 208. Thecomparison module 210 generates marker detection data 212 indicatingwhich markers are determined to be present based on the most recentmonitoring data and the user profile for the user 102 a. The markerdetection data 212 is used, along with the various other collected data,to make predictions and generate scores for the user 102 a.

In stage (G), the computer system 110 generates user scores 240 for theuser 102 a for various aspects of disease management. These can includeuser scores 240 for the user's exposure level 241 to a disease, theuser's susceptibility to the disease 242 if exposed, and a likelihood ofinfection 242 that the user is currently infected. The computer system110 can use a variety of information to generate these scores, includingthe user profile, monitoring data, the marker detection data,environmental data, and community data.

The computer system 110 can derive feature values 220 from thesedifferent data sources and provide the feature values 220 to one or morepredictive models 230, which have been trained based on data sets ofmany different individuals and their status with respect to the diseaseover time. The predictive models 230 maybe machine learning models, suchas neural networks. In addition or as an alternative, the models 230 maybe rule-based models, statistical models, or other types of models. Theoutputs of the predictive models 230 can be values that indicatepredictions with respect to the disease, for example, exposure level241, susceptibility 242, and likelihood of infection 243. A differentmodel 230 can be used for each type of prediction.

The exposure level score 241 can be a measure of the likelihood,frequency, duration, and/or intensity at which the user 102 a ispredicted to have been exposed to COVID-19. In some cases, the exposurelevel score 241 is an exposure risk score indicating the user's level ofrisk of being exposed to the disease given the user's current behaviorpatterns, the disease prevalence and transmission patterns in the user'scommunity, and other data. The model 230 that is trained to predictexposure level can be trained based on monitoring data for many usersand the corresponding outcomes of contracting the disease, allowing themachine learning training process to train the model 230 to predict thecombinations of markers, community measures, environmental measures,monitored physiological and behavioral measures, etc. that indicatedisease exposure.

In calculating exposure level, the computer system 110 can use varioustypes of location tracking information for the user 102 a, to indicaterecent travel and activities of the user 102 a over one or more timeperiods (e.g., the previous day, the previous 3 days, the previous week,the previous two weeks, etc.). In some cases, data describing travel andtracked locations of the user 102 a can be provided directly to apredictive model 230. For example, the feature values 220 may includeindications of visits to specific places at specific times. In othercases, summary data or aggregate data characterizing the overall type,nature, or pattern of travel may be provided, such as a summary of thetypes of locations visited (e.g., grocery store, movie theater, outdoorpark, etc.) and the number of visits and/or duration of time spent atthe respective types of locations. In other words, the computer system110 may optionally perform various pre-processing steps on the rawlocation tracking data to characterize the nature of the travel andactivities of the user in order to determine feature values reflectiveof the user's actual travel and/or the user's typical travel habits(e.g., on a daily, weekly, or monthly basis).

Among the types of data that the computer system 110 can use todetermine the feature values for the model(s) 230 used to predictexposure level include: locations visited, types or classifications ofthose locations, times of the visits, durations of the visits, modes oftransportation (e.g., walking, public transportation, private vehicle,airplane, etc.), estimated or predicted occupancy of locations andtransportation modes, and so on. Much of this information can beinferred by correlating GPS location data from the user device 104 awith map data (e.g., to determine the location and location typecorresponding to time spent at a certain GPS-specified region), and bydetermining which transportation mode has a motion profile matches themotion detected (e.g., a trip segment having movement along roads withan average speed of 30 mph could be classified as driving in a privatevehicle). Other data, such as occupancy estimates, can be determinedfrom EMAs or in-the-moment surveys using the user device 104 a (e.g.,asking, “roughly how many people are in the store today?”) or throughhistorical averages for transportation modes and for locations orlocation types. The computer system 110 can also access thecommunity-level disease data for the user's community and for any othergeographic regions that the user visited, to determine diseaseprevalence rates for those regions (e.g., COVID-19 infection rates,COVID-19 transmission rates, data quantifying COVID-19 positive testsand negative tests and changes in them over time, COVID-19 infectiontrends, etc.). This community level data, combined with map datadescribing regions and the user's travel data enable the computer system110 to determine the amount of time the user spent in different areas,how frequently the user visited, what the activities of the user were,and what the local disease prevalence is in those visited areas.

For example, the computer system 110 may determine, from the locationtracking data and map data sources and community data, that over thelast week (1) the user 102 a spent 2 hours at a particular grocery store(e.g., most likely shopping around other customers), in a region thathas a high prevalence of COVID-19 and increasing numbers of positivetests over the last week, and (2) the user 102 a spent 4 hours in aparticular outdoor park in a region having low prevalence of COVID-19and a steady or decreasing number of positive tests over the last week.The computer system 110 then provides this information to the machinelearning model 230 at the level of detail that the model 230 has beengenerated and trained to accept information, which may be at anaggregate or summary level (e.g., among other feature values, a featurevalue of “2” for a feature representing the number of hours spent in ahigh disease prevalence area, and a feature value of “4” representingthe number of hours spent in a low disease prevalence area). On theother hand, the model 230 may be generated and trained to process moredetailed information, such as features indicating the types of locations(e.g., grocery store, public outdoor park, etc.), the specific locationsvisited (e.g., the specific grocery store and specific part visited),the times and durations of individual visits, the modes and routes oftransportation, more specific community disease measures for thelocations visited, and so on. From this and other information about theuser, a trained model 230 can predict the exposure level 241 or risklevel that the user 102 a has or will contract COVID-19. Machinelearning models such as neural networks are well-suited to this type ofprediction, and with example training data based on the locationtracking data and community disease measures for many differentindividuals (as well as disease outcomes for those individuals, e.g.,when and whether they tested positive or developed COVID-19 symptoms),the models 230 can be trained to learn the factors and relationshipsthat increase exposure to and risk of contracting COVID-19.

Contact tracing data can optionally be used in this exposure predictionprocess, e.g., with contract tracing results used to provide featurevalues to the models 230 (e.g., indicating how many people determined tohave COVID-19 the user 102 a actually came near, when, and for howlong), or to weight or adjust the exposure level score 241 of a model230 (e.g., to boost the exposure level when contact tracing showscontact with one or more infected people, with the boost amount scaledby the number of instances and duration of contact).

The disease susceptibility score 242 can indicate a predicted level ofvulnerability of the user 102 a to COVID-19 if the user were to contractthe disease. For example, separate from the user's exposure level, thesusceptibility may be an estimate or predicted measure of how vulnerablethe user would be would be to the disease (e.g., the degree that theuser may be affected, such as the expected severity of the disease) ifthe user were to be exposed to the disease and/or if the user were tobecome infected. This could reflect factors such as the likelihood thatthe user would contract the disease if exposed, the likelihood ofexperiencing different symptoms of the disease if the user becameinfected, the likely severity of those symptoms, the risks of death orlong-term impairment due to the disease, etc. In the example, thesusceptibility score 242 is shown as a numerical value. Thesusceptibility score 242 could be, for example, indicative of a riskcategory (e.g., high, medium, or low risk of a certain level of harmoccurring). For example, one or more models 230 may provide valuesindicating the relatively likelihood that different risk categories areapplicable for a user (e.g., 0.1 for low risk, 0.6 for medium risk, 0.3for high risk, showing that the medium risk category is mostappropriate). The susceptibility measure could be a prediction of alikelihood that a user, if infected, would have a symptomatic caserather than an asymptomatic case. The susceptibility prediction may bemade in general (e.g., a level of severity for disease effects as awhole), or with respect to specific outcomes or harms of the disease(e.g., a prediction of risk of stroke, a prediction of risk ofhospitalization needed, a risk of sepsis, a risk of death, etc.). Forexample, a general susceptibility score 242 may be provided to indicatethe risk level for the user 102 a experiencing a severe case ofCOVID-19, according to a predetermined set of criteria. Additionalmeasures or scores may indicate risk levels or likelihoods for each ofmultiple different symptoms or outcomes.

The model 230 used to predict the susceptibility score 242 can betrained using, for example, monitoring data and health record data forindividuals that contracted COVID-19. Data characterizing the symptoms,outcomes, and recovery profiles of those individuals can be assessed toassign a score (e.g., on a scale of risk or disease severity) as a labelfor that data set. During training, the each example input (e.g.,providing data about user attributes, user behaviors, and other userparameters for a user) can be provided as input to a model 230, and theassigned risk score labels (e.g., reflecting symptoms and outcomesexperienced) can be used as the target outputs that the model is trainedto predict.

The infection likelihood score 243 indicates a likelihood that a user iscurrently infected with COVID-19. The model 230 to predict infectionlikelihood can use information about changes in behavior, as well asphysiological monitoring data and user-reported information, to detectlikely infections before traditional symptoms are detected. For example,changes in user physical activity, cognitive measures, and mood relativeto the user's personal baseline measures can be used to identifyinfection at very early stages. Changes in behavior (e.g., changes indiet, activity levels, movement patterns, sleep patterns, etc.) may notindividually be conclusive indicators of infection, but subtle changestaken together can demonstrate combinations or progressions ofobservations that are consistent with infection. By placing behaviorchanges and physiology changes in context of short-term (e.g., hours,days or weeks) and long-term personal baseline measures (e.g., weeks,months, or years), the model has information to be able to moreaccurately assess risks. In addition, detection of changes with respectto baseline levels provides the computer system 110 an opportunity toconfirm or obtain further context about those changes. For example, whena user's behavior changes are detected through passive sensing andcomparison to baseline measures, the computer system 110 can act inresponse to initiate EMAs or other surveys or interactions to ask theuser 102 a about those changes, e.g., the cause of the changes, whetherthe user noticed the changes, whether there are related changes, etc.This additional data can confirm the significance of changes or allowextraneous or non-disease-related changes to be filtered out ordiscounted.

To predict infection likelihood, collected data for the user 102 a fromvarious sources (e.g., health records, user input such as EMA responses,sensor data, treatment data, user demographic data, and so on) can beused. The collected data used can be data for a current time period(e.g., the current day, week, etc.) as well as collected data for priortime periods and/or long-term historical data. These data items may beinput to a model 230 or analysis process in any of various ways, e.g.,as measurement values (e.g., a body temperature measurement of 99.0°),as classifications of measurements or data patterns (e.g., activitylevel is high, medium, or low), as binary feature values (e.g., whetheran event or condition was detected or not), as indications of whethermarkers were detected in the user's data (e.g., according to the markerdefinitions 208), as a count of how many times an event or behavioroccurred, as an aggregate measure (e.g., an average, maximum, minimum,etc.) based on multiple observations, as values indicating whether andto what extent collected data varies from personalized baseline measures(and/or values providing the personalized user baseline measures), andso on. Detected markers for signs or symptoms of a disease can beindicated and used as input also, for example, as a result ofpre-processing the collected data set for a user and comparing collecteddata patterns with criteria of marker definitions to see which markersare reflected in the collected data. Baseline measures for a user,demographic attributes of the user, and other types of data can also beprovided as input to the infection likelihood determination.

There are many potential symptoms of COVID-19, and additional symptomsare being identified over time. Examples include coughing, shortness ofbreath or difficulty breathing, fatigue, muscle or body aches, headache,new loss of taste or smell, sore throat, congestion or runny nose,nausea or vomiting, diarrhea, skin rash, blood clots, stroke, cognitiveimpairment or confusion, and so on. The predictive models 230 may useinstances of detecting these symptoms, or data patterns from which thesesymptoms may be inferred, in making predictions, but it is not limitedto these. Indeed, the infection prediction model 230 can usephysiological measures and changes, behavioral measures and changes, andother data to detect infection with high confidence, even if symptomsare not specifically identified and called out. Further, when symptomsare used, contextual data, baseline data, and data characterizing thesymptoms (e.g., not just that a headache occurred, but the timing,duration, and severity of the headache) to distinguish between symptomsthat are likely caused by COVID-19 and those that are not.

The models 230 used to predict infection can take into account theexposure estimates for the user. For example, a score or other output ofan exposure estimation model may be used as an input to the infectionprediction model. In addition, or as alternative, the activities of auser and locations a user has been, as well as the infection rates inthe user's community, can be used to determine the likelihood ofinfection. For example, an elevated body temperature may generally be asign of potential COVID-19 infection. But if the user's community has avery low prevalence of COVID-19 (e.g., according to community measuresfor infection rate, disease prevalence, etc.) and/or the user'sactivities indicate very low contact with others (e.g., based onlocation tracking of the user's phone and smartwatch, from user-reportedsurvey answers, and/or contact tracing data), the likelihood ofinfection may be indicated to be lower than it would otherwise be forsomeone with more travel or someone in an area with higher prevalence ofthe disease. Thus, at least in some implementations, the computer system110 can weight the detected signs and symptoms of a disease (or simplythe data patterns indicative of infection risk) according to thecommunity data related to the disease.

In some implementations, the predictive models 230 for predictinginfection likelihood and/or other items may receive indicators ofcommunity characteristics and community exposure levels, and thetraining of the models 230 may automatically account for the variationin the predicted item due to factors such as user locations, useractivities, user exposure level, community characteristics, communityexposure levels, etc. In other words, the predictive models 230 can betrained to receive and process input feature values indicative of any ofthese factors, and potentially any or all of the data items collectedfor the user 102 a. Through the model training process, the models 230learns the relative importance and predictive value of different typesof input, as well as how different combinations of input values increaseor decrease likelihoods, so that the trained models 230 generatepredictions using the relationships implicitly learned through machinelearning training from a training data set showing many examples ofother users (e.g., training data sets describing different users, theirdisease outcomes, their monitoring data, their communities' diseasemeasures, and so on).

Along with each prediction, the computer system 110 may provide acorresponding confidence score 245 indicating how confident the system110 is the assigned score. The confidence scores may be based on variousfactors, such as variability in the model or data patterns used intraining the model. The confidence score there also based on the tape,quantity, and quality of data available for the user. In someimplementations, computer system 110 can determine when additional datais needed to Increase the level of confidence in the scores, and canautomatically take actions to acquire they needed data. For example, thecomputer system 110 may identify gaps in the monitoring data obtainedfor a user, and take action to request the missing information from theuser, request a change in monitoring and reporting actions of the userdevice 104 a to provide the missing data, and so on.

The user scores 240 can be predicted measures for the user at thecurrent time. For example, the infection likelihood score 243 can be ascore indicating the predicted likelihood that the user is currentlyinfected. Nevertheless, other scores can be generated using similartechniques to predict measures for different time periods. For example,a model 230 can be trained to predict whether a user has previously beeninfected. Similarly, a model 230 can be trained to predict whether auser will become infected over a certain time in the future (e.g., thenext day, the next week, the next month, etc.), given the currentphysiological attributes of the user 102 a, the observed behaviorpatterns of the user 102 a, and community disease measures (e.g.,disease prevalence, infection rate, infection rate trends, etc.). Thiskind of time offset for predictions can be built into a model 230 bytraining the model with training examples that themselves have a builtin time offset. For example, examples can be used that include (i) dataof users and their communities up for respective time periods, and (ii)training targets or ground truth labels that indicate outcomes not atthe end of the time periods, but at some time in the future (e.g., thenext day, the next week, the next month, etc., depending on the timeframe for which prediction is desired).

In some implementations, the user scores 240 are communicated to theuser 102 a, for example, provided through a notification, a userinterface of an application running on the user device 104 a, etc. Theuser scored 240 may also be used to select messages to the user even ifthe scores themselves are not provided. For example, if the user'ssusceptibility score is above a threshold, this may be communicated tothe user 104 a along with recommendations or instructions forpreventative measures. As another example, when the infection likelihoodscore 243 exceeds a predetermined level, the user 104 a can be provideda notification that the user is likely infected and should monitorcertain potential symptoms, avoid contact with others, etc. The userscores 240 and collected monitoring data can be provided to the user'sdoctor, stored in the user's medical records, and, if the user is aparticipant in a research study, provided to researchers involved in thestudy.

In stage (H), the computer system select from among various controlactions to perform to improve monitoring and prediction for the user 102a. For example, the computer system 110 can store rules, decisionmodels, and thresholds used to select from among a set of actions 250,such as updating monitoring procedures, changing and supplementingdownloaded modules 112, recommending monitoring devices, recommendinglaboratory tests, etc. As an example, the system can change or adapt thedata collection or disease monitoring actions performed for the user 102a according to the predictions made for the user. Other diseasemanagement actions that can be based on the user scores 240 are shownand discussed with respect to FIG. 5.

The computer system 110 can store and use rules 251, decision models252, threshold 253, or other techniques to determine how to adjust themonitoring and data collection actions for the user 102 a. This allowsfor multiple levels of testing and monitoring, where the level ofmonitoring changes over time according to the conditions detected andthe predictions made. As more monitoring data is collected and thepredictions for a user change, the system can make progressive changesin monitoring, including through sensor data collection, providingsurveys and direct user interactions with digital devices, requestinglaboratory tests or COVID-19 tests (e.g., nasal swab test, antibodytests, etc.).

FIG. 4 shows an example decision tree that the computer system 110 canuse to vary the level of monitoring according to the conditions detectedand risk levels predicted. Although depicted as a decision tree here,this is simply to illustrate the changes in actions for differentconditions more clearly. The data used to drive the selection andadjustment of monitoring actions (or treatment actions as discussedbelow) can be stored in any appropriate data structure or format. Forexample, the data can be provided as a decision tree, a table, a set ofrules, a machine learning model configured to classify or predict whichactions are appropriate given input about a user and the user'scommunity, and so on.

In general, the computer system 110 can adjust the intensity and type ofmonitoring to provide more intensive and heightened monitoring asfactors such as exposure level, likelihood of infection, susceptibilityto disease, and so on. Other decision trees or selection processes canbe used for those factors, or a combined decision process can take intoaccount all of the various predictions and monitored factors. Thecomputer system 110 can thus automatically tailor the burden on the userand the intensity of monitoring to increase or decrease the level ofmonitoring and interaction with the user, as the situation or predictedrisk levels for the user change. As FIG. 4 shows, there are differentlevels of monitoring for signs and symptoms of COVID-19 (e.g., Level 1,Level 2, etc.), each of which may involve measuring differentphysiological parameters, different behavioral parameters, differentmental health or cognitive parameters, and so on. Similarly, differentmonitoring levels may involve different frequency, intensity, orprecision of data collection.

As an example, if a user's exposure score 241 indicates that theexposure level is less than five, monitoring continues at a minimum ofLevel 1. As monitoring data comes in (e.g., through sensor data for theuser and self-reported data for the user), and community statisticalreports for COVID-19 are updated, the user's exposure level can bere-evaluated. If the exposure level is determined to be greater than 10but not greater than 15, then the system causes the user device 104 a toperform monitoring at Level 2, which may include activating a differentset of sensors (e.g., using more sensors), recording data morefrequently, and so on. The system also causes the user device 104 a toprovide a social activity survey to better understand the user'sexposure risk. The data from this enhanced monitoring and from thesurvey provides new information, which causes the system to againre-evaluate the exposure level for the user. Based on the result, thesystem may dial back monitoring to Level 1 if the exposure leveldecreases below 5, may maintain the monitoring level at Level 2 if theexposure level remains between 5 and 10, or increase the monitoringlevel if the exposure level is predicted to be greater than 10.

FIG. 5 shows how the system 100 can use collected data and generateduser scores 240 to select and execute various actions. For example, thesystem 100 (e.g., through operations of the user device 104 a and/or thecomputer system 110) can select a testing kit from among variousdifferent testing kit options. As another example, the system 100 canselect a vaccine and vaccine administration procedure from among variousoptions. As another example, the system 100 can select a digitaltherapeutics program for the user 102 a from among multiple differentdigital therapeutics programs. As an example, the system 100 can selectmedications and other treatment actions from among multiple differentoptions. The system 100 can provide recommendations for the selecteddisease management options to the user 102 a and/or to a physician orother medical professional. In some implementations the system 100carries out the treatment action directly by ordering a test kit,changing monitoring or other interaction options through the user device104 a, etc. In the example of FIG. 5, based on the user scores 240 andother collected information 510, and using the techniques discussedbelow, the computer system 110 selects to provide the user 102 a with(i) the COVID-19 testing kit labeled “Kit 1,” (ii) a vaccine optionincluding two doses of “Vaccine 1,” and (iii) digital therapeutics usinga digital therapeutics program “Program 1.” The user's infectionlikelihood score does not indicate a high-confidence diagnosis ofCOVID-19 yet or the need for drugs to be prescribed, so the system doesnot select any of the drug options or other therapies to be provided tothe user 102 a. Of course, if the testing kit results indicate that theuser 102 a has contracted COVID-19, or as other information is obtainedabout the user 102 a, then the computer system 110 can re-evaluate andmay recommend a drug option at that time, as well as re-evaluate whethera vaccine is appropriate and whether the digital therapeutics optionshould be changed.

The computer system 110 can use the user scores 240 described in FIG. 3,as well as other information about the user 102 a and the user'scommunity, to select disease management actions. The computer system 110can include different models 530 a-530 d used to select differentdisease management actions. In some implementations, selection ofdisease management actions for a user can be made based on only a fewfactors, such as user scores. For example, the decision whether a usershould receive a disease testing kit or a vaccine may be based on theuser scores 240 for exposure, susceptibility, and/or infectionlikelihood. In some implementations, more information about a user canbe incorporated for more complex decisions, such as which of multipletesting kits to use, which of multiple vaccine options to provide, whichof multiple digital therapeutics to provide, which medications or othertherapies to provide and the parameters for them (e.g., frequency,dosage or intensity, etc.), and so on. This can include using a userprofile that shows user history and long-term attributes (e.g., age,sex, height, weight, race or ethnicity, chronic conditions, medicalhistory, user residence address and contact information, etc.),monitoring data (e.g., recent or current physiological data, behavioraldata, etc., and patterns and changes in them), and so on.

The various collected data 510 for a user 102 a can be processed andused in action selection 511, and then carried out in an actionexecution 512 process. As discussed further below, the computer system110 can store various information to aid in selection of appropriatedisease management actions, such as selection models 530 a-530 d, datatables 540 a-540 d, sets of selection criteria 541 a-541 d for differentactions, as well as profiles 542 a-542 d for different options. Once thecomputer system 110 determines that a disease management action isappropriate or needed for the user 102 a, the action execution module512 can cause that action to be carried out, sometimes directly by thecomputer system 110, other times through communication with the userdevice 104 a or third-party systems, or by making recommendations to theuser 102 a, medical care providers, or others. For example, once atesting kit is selected for the user 102 a, the computer system 110 canorder the kit and have it delivered to the user 102 a. As anotherexample, once a vaccine option is selected for the user 102 a, thecomputer system 110 can schedule an appointment for the user 102 a toreceive the vaccine (e.g., initiate interactions through the user device104 a for the user 102 a to select and confirm an appointment locationand time). As another example, the computer system 110 can causeselected digital therapeutics to be delivered through the user device104 a, whether through instructing specific interactions, providingsoftware or configuration data for the user device 104 a to determineappropriate interactions, enrolling the user 102 a in a program ofdigital therapeutics, etc. Finally, if pharmaceuticals or othertreatments are needed, the computer system 110 can provide digitalinteractions through the user device 104 a to educate the user andinstruct proper usage, order the needed drugs and doses for delivery tothe user 102 a, order needed monitoring device or medical treatmentdevices, schedule prescription pickup at pharmacies or scheduleappointments with doctors, etc.

Examples of selecting disease management actions will now be discussed.For example, a testing kit selection model 530 a can be used todetermine whether a testing kit should be provided for the user 102 a,and if so, which one is most appropriate. The selection model 530 a canbe, for example, a set of rules, a decision tree, a lookup table, atrained machine learning model, etc. In some cases, the determinationwhether to send a testing kit may simply be based on the user scores240, for example, whether one or more of the user scores 240 satisfies acorresponding threshold. More complex decision-making models can beused, for example, taking into account measures of disease prevalenceand spread in the user's community, using patterns and trends inmonitoring data for the user, and so on, potentially using any of thetotality of information about the user.

The associated table 540 a identifies different COVID-19 testing kitoptions. These may represent, for example, different types of testingkits (e.g. nasal swab testing kits, throat swab testing kits, fingerprick blood test kits, etc.), kits from different manufacturers, kitswith different numbers of tests included, and so on. For each testingkit option, there is a set of criteria 541 a that can describe userattributes or other factors that make a kit indicated or contraindicatedfor use. For example, some kits may be appropriate for (or havedifferent accuracy of testing for) different stages of COVID-19, or maybe appropriate for different age ranges of individuals, or may beappropriate for different combinations of physiological or behavioralattributes of users. In addition, or as an alternative, profiles 542 afor the kits can be stored and updated, and the profiles 542 a mayindicate characteristics of the kits and their results. For example, theprofiles 542 a may indicate false positive rates, false negative rates,availability of the kits in different geographic regions, indications,contraindications, measures of the difficulty or burden for using thetest, the time or delay involved in obtaining results, effectiveness ofthe test for different populations, and other characteristics.

The selection model 530 a may use the criteria 541 a and/or the kitprofiles 542 a to determine which kit option is best for the user 102 a,given the monitoring data for the user and other data available for theuser 102 a. For example, the computer system 110 can compare user datawith the data in the profiles or criteria to determine which option bestmatches the user's collected data and user profile. As another example,if a machine learning model is trained for testing kit selection, thecomputer system 110 can provide input feature values derived from theuser data (e.g., values indicating user age, sex, location,physiological attributes, or other data relevant to kit selection) tothe model and receive outputs indicative of the likelihoods thatdifferent testing kit options are appropriate for the user. In somecases, multiple kits having different effectiveness profiles may beselected, to provide additional accuracy or verification of results.Once a kit option is selected for the user 102 a, the computer system110 causes the selected kit to be ordered and shipped to the user 102 a.

In a similar manner to selection of the testing kits, the computersystem 110 can also select vaccines, digital therapeutics (e.g.,programs or combinations of therapeutic interventions, or specificinterventions), pharmaceuticals for treatment, devices for monitoringthe user 102 a, devices for treatment, settings for devices fortreatment, and so on, even for other decisions (e.g., such as when theuser should rest, when the user needs to visit a hospital or a doctor,when the patient may need ventilator support, etc.). For each, acorresponding set of options can be determined, each with criteria orprofiles being determined to specify the conditions when the variousoptions are indicated. In addition, or as an alternative, the system cantrain machine learning models to score the appropriateness of thevarious options, without explicitly specifying the criteria orconditions in which different options are appropriate.

For example, a vaccine selection model 530 b can be used to determinewhether a vaccine should be provided for the user 102 a, and if so,which one is most appropriate. The selection model 530 b can be, forexample, a set of rules, a decision tree, a lookup table, a trainedmachine learning model, etc. In some cases, the determination whether toprovide a vaccine for the user 102 a may simply be based on the userscores 240, for example, whether one or more of the scores 240 satisfiesa corresponding threshold. In particular, high susceptibility scoresand/or exposure scores may indicate increased need for a vaccine, aswould high levels of COVID-19 prevalence indicted by the community data.More complex decision-making models can be used, for example, takinginto account measures of disease prevalence and spread in the user'scommunity, using patterns and trends in monitoring data for the user,and so on, potentially using any of the totality of information aboutthe user.

The associated table 540 b identifies different COVID-19 vaccineoptions. These may represent, for example, different types of vaccines(e.g., live-attenuated vaccines, inactivated virus vaccines, subunitvaccines, recombinant vaccines, polysaccharide vaccines, conjugatevaccines, etc.), different modes of administration (e.g., hypodermicneedle vs oral ingestion), vaccines from different manufacturers,vaccine administrations with different parameters (e.g., differentdosages, different numbers of doses, different spacing between doses,etc.) and so on. For each vaccine option, there is a set of criteria 541b that can describe user attributes or other factors that make a vaccineoption indicated or contraindicated for use. For example, some vaccinesmay be appropriate for (or have different levels of effectiveness for)individuals in different age ranges, individuals with different chronicconditions, for individuals with different combinations of physiologicalor behavioral attributes, etc. In addition, or as an alternative,profiles 542 b for the vaccine options can be stored and updated, andthe profiles 542 b may indicate characteristics of the vaccines andtheir results. For example, the profiles 542 b may indicateeffectiveness statistics (e.g., including effectiveness of the vaccineoption for different populations), duration of protection provided,availability of the vaccines in different geographic regions,indications or contraindications, potential side effects, and othercharacteristics.

The vaccine selection model 530 b may use the vaccine option criteria541 b and/or the vaccine option profiles 542 b to determine whichvaccine option is best for the user 102 a, given the monitoring data forthe user 102 a and other data available for the user 102 a. For example,the computer system 110 can compare user data with the data in theprofiles 542 b or criteria 541 b to determine which vaccine option bestmatches the user's collected data and user profile. As another example,if a machine learning model is trained for vaccine option selection, thecomputer system 110 can provide input feature values derived from theuser data (e.g., values indicating user age, sex, location,physiological attributes, or other data relevant to vaccine selection)to the model 530 b and receive outputs indicative of the likelihoodsthat different vaccine options are appropriate for the user 102 a. Oncea vaccine option is selected for the user 102 a, the computer system 110may cause the selected vaccine to be ordered and shipped foradministration, as well as notify the user 102 a and his or her doctor.

As another example, a digital therapeutics model 530 c can be used todetermine whether digital therapeutics should be provided for the user102 a, and if so, which interventions or programs are most appropriate.The selection model 530 c can be, for example, a set of rules, adecision tree, a lookup table, a trained machine learning model, etc. Insome cases, the determination whether to provide digital therapeuticsfor the user 102 a may simply be based on the user scores 240, forexample, whether one or more of the scores 240 satisfies a correspondingthreshold. More complex decision-making models can be used, for example,taking into account measures of disease prevalence and spread in theuser's community, using patterns and trends in monitoring data for theuser, and so on, potentially using any of the totality of the collectedinformation 510 for the user.

The associated table 540 c identifies different COVID-19 digitaltherapeutics options. These may represent, for example, differentcategories of interventions (e.g., posture therapy, breathing therapy,mental health therapy, exercise therapy, sleep therapy, nutritiontherapy, and so on), specific interventions (e.g., different discreteitems of media, questions, forms, etc.), different programs (e.g., careplans or ongoing campaigns of therapy), different modes of communication(e.g., visual, audible, text, video, etc.), digital therapeutics ofdifferent levels of dosage or intensity, and so on. For each digitaltherapeutics option, there is a set of criteria 541 c that can describeuser attributes or other factors that make a digital therapeutics optionindicated or contraindicated for use. For example, some digitaltherapeutics may be appropriate for (or have different levels ofeffectiveness for) individuals in different age ranges, individuals withdifferent chronic conditions, for individuals with differentcombinations of physiological or behavioral attributes, etc. Inaddition, or as an alternative, profiles 542 c for the digitaltherapeutics options can be stored and updated, and the profiles 542 cmay indicate characteristics of the digital therapeutics and theirresults, both positive and negative (e.g., side effects). For example,the profiles 542 c may indicate effectiveness statistics (e.g.,including effectiveness for different populations), duration of effector benefit provided, availability of the digital therapeutics indifferent geographic regions, indications or contraindications,potential side effects, and other characteristics.

The digital therapeutics selection model 530 c may use the digitaltherapeutics option criteria 541 c and/or the digital therapeuticsoption profiles 542 c to determine which option(s) are best for the user102 a, given the monitoring data for the user 102 a and other dataavailable for the user 102 a. For example, the computer system 110 cancompare user data with the data in the profiles 542 c or criteria 541 cto determine which digital therapeutics option best matches the user'scollected data and user profile. As another example, if a machinelearning model is trained for digital therapeutics option selection, thecomputer system 110 can provide input feature values derived from theuser data (e.g., values indicating user age, sex, location,physiological attributes, behavior patterns, baseline measures, or otherdata relevant to digital therapeutics selection) to the model 530 c andreceive outputs indicative of the likelihoods that different digitaltherapeutics options are appropriate for the user 102 a. The computersystem 110 can also re-calculate the selection periodically or inresponse to additional collected data for the user 104 a, and can add,remove, or modify which digital therapeutics are provided in response.Once a digital therapeutics option is selected for the user 102 a, thecomputer system 110 may cause the selected digital therapeutics to beprovided, for example, by providing software, instructions, programmodules, configuration data, and so on to the user device 104 a to causeappropriate interventions to be provided.

As another example, a drug selection model 530 d can be used todetermine whether pharmaceuticals should be provided for the user 102 a,and if so, which drugs, drug administration regimens, and drugcombinations are most appropriate. In addition to or instead ofselection of drug options, the computer system 100 can characterize andselect from among other treatment and therapy options, such asventilator support options, antibody therapy options, and so on, usingthe same techniques. The selection model 530 d can be, for example, aset of rules, a decision tree, a lookup table, a trained machinelearning model, etc. In some cases, the determination whether to providedrugs for the user 102 a may be based at least in part on the userscores 240, for example, whether one or more of the scores 240 (e.g.,the infection likelihood score 243) satisfies a corresponding threshold.More complex decision-making models can be used, for example, takinginto account medical history data, patterns and trends in physiologicaland behavioral monitoring data for the user, and so on, potentiallyusing any of the totality of the collected information 510 for the user.

The associated table 540 d identifies different COVID-19 drug options.These may represent, for example, different categories or classes ofdrugs, specific drugs, different regimens or administrations of drugs(e.g., combinations of different parameters for dosage, frequency,administration with or without food, etc.), drugs known to havedifferent side effects or effectiveness profiles, and so on. For eachdrug option, there is a set of criteria 541 d that can describe userattributes or other factors that make a drug option indicated orcontraindicated for use. For example, some drug options may beappropriate for (or have different levels of effectiveness for)individuals in different age ranges, individuals with different chronicconditions, for individuals with different combinations of physiologicalor behavioral attributes, etc. In addition, or as an alternative,profiles 542 d for the drug options can be stored and updated, and theprofiles 542 d may indicate characteristics of the drugs and theirresults, both positive (e.g., in reducing symptoms and speeding uprecovery) and negative (e.g., side effects). For example, the profiles542 d may indicate effectiveness statistics (e.g., includingeffectiveness for different populations), duration of effect or benefitprovided, availability in different geographic regions, indications orcontraindications, potential side effects, and other characteristics.

The drug option selection model 530 d may use the drug option criteria541 d and/or the drug option profiles 542 d to determine which option(s)are best for the user 102 a, given the monitoring data for the user 102a and other data available for the user 102 a. For example, the computersystem 110 can compare user data with the data in the profiles 542 d orcriteria 541 d to determine which drug option best matches the user'scollected data and user profile. As another example, if a machinelearning model is trained for drug option selection, the computer system110 can provide input feature values derived from the user data (e.g.,values indicating user age, sex, location, physiological attributes,behavior patterns, baseline measures, or other data relevant to drugselection) to the model 530 d and receive outputs indicative of thelikelihoods that different drug options are appropriate for the user 102a. The computer system 110 can also re-calculate the selectionperiodically or in response to additional collected data for the user104 a, and in response can add, remove, or modify which drug(s) areprovided. With drug selection and any other disease management action,the system 110 can inform a physician of the system's determinations andrecommendations, and may require approval of a physician beforeimplementing the selected option. Once a drug option is selected for theuser 102 a (and approval of a physician is obtained if needed), thecomputer system 110 may cause the selected drug to be provided, forexample, by communicating a prescription to a pharmacy system, enteringthe recommendation or prescription into medical records, communicatingthe recommendation and usage instructions to the user 102 a, orderingdoses of the medication to be delivered to the user 102 a, etc.

FIG. 6 illustrates additional aspects of the system 100. Although theexample shows actions for a single user 102 a, the computer system 110can be used to concurrently monitor and treat one or more diseases formany different users in different geographical areas.

As discussed above, the computer system 110 can select and provide adata package module 112 to the user device 104 a, which specifies howthe user device 104 a and potentially other devices interact with theuser and collect information (e.g., sensor data, survey responses,etc.). The module 112 can be received by a client device from a serverin order to treat a patient or participant, or more generally to managea disease (e.g., monitor exposure, detect infection, monitor diseaseeffects, minimize symptoms, improve recovery, and so on). The module 112can be downloadable, such as an mobile device application or an add-onmodule or configuration data that supplements or augments an applicationand alters how the application operates. The module 112 can run on userdevices, such as a phone, tablet computer, smart watch, web browser,etc.

The module 112 can be configured to acquire information that wouldindicate the presence or absence signs and symptoms of a disease. Themodule 112 can be used to detect events and conditions (e.g., behaviors,physiological attributes, patterns, and so on) that are indicative ofexposure to a disease or infection with a disease, so that exposurelevels and infection by a person can be determined. For example, themodule 112 can include rules or code to cause detection of specificsigns and symptoms of a specific disease, such as COVID-19. The module112 may additionally be configured to monitor progress of a disease andtreatment. In other words, a module 112 may be a treatment package thatis used during treatment, after a user has been diagnosed to have thedisease, to monitor the effects of treatment and monitor effects of thedisease, so that the computer system 110 and/or the module 112 cansuggest and in some cases implement changes to treatment.

The computer system 110 can create a profile or user account for theuser to store collected information, for example, once the user 102 asigns up or enrolls to participate in disease management. Once themodule 112 is downloaded, a client application is adjusted based on themodule 112 to monitor the user 102 a using measures that are locallymonitored through various techniques, such as (i) user input data 602indicating responses to ecological momentary assessments (EMAs) or othersurveys or prompts provided by the system 100, (ii) device usage dataindicating how the user device 104 a or other devices are used, and(iii) sensor data 604 from the device 104 a or other devices (e.g., awearable device 107 a, a medical monitoring device 108 a, etc.). Thecomputer system 110 also obtains additional information for the user 102a, such as EMR/EHR data 606, including potentially treatment data 608that indicates how a user is being treated for the disease beingmonitored. The EMR/EHR data 606 can be used for various purposes,including to determine baseline measures for a user and to determineexisting conditions and comorbidities, which the computer system 110 canuse to make predictions and estimates (e.g., to estimate thesusceptibility or risk that a disease poses for the user 102 a) and toadjust decisions for the user (e.g., about which testing kit, vaccine,module 112, medication, digital therapeutics, monitoring procedures, orother intervention is recommended or provided by the computer system110).

The computer system 110 can also acquire community data 610 thatdescribes the community in which the user 102 a resides, works, orotherwise spends time. The community data 610 can include dataindicating population levels, demographic information, types oflocations present in the region, mapping data, and so on. The communitydata can also include measures that aid and assist in determiningexposure risks, infection or exposure hotspots, and general diseaseoutbreak related conditions. For example, the community data can includeinfection rates for COVID-19 for different regions of the community,trends of COVID-19 infection rates in the community, treatment outcomesin the community (e.g., death rates, hospitalization rates, etc. due toCOVID-19), and so on. The same type of information can be obtained fornearby regions or other regions having similar populationcharacteristics.

The community data 610, as well as the other data collected (e.g., data602, 604, 606, 608, and so on), be updated and refreshed periodically onan ongoing basis. As new collected data for the user 102 a is received,the computer system 110 may re-evaluate the predictions for the user(e.g., exposure level, susceptibility to the disease, likelihood ofinfection, etc.) and also re-evaluate the actions that the computersystem 110 recommends or carries out for the user 102 a.

In some implementations, the module 112 that is downloaded to the userdevice 104 a includes rules, logic, models or other elements tointerpret and act on monitored data. The monitored information isreceived and measured using data that characterizes signs and symptomsof a disease, and the data to specify indicators of a disease may beincluded in the module 112 or may be downloaded later through updates tothe module 112 or as other supplemental information provided over thecommunication network. The module 112 can include data specifying ordefining markers to look for in order to detect a disease such asCOVID-19. The set of markers can be dynamically updated throughcommunication of the user device 104 a with the computer system 110. Thecomputer system 110 may maintain, update, and distribute definitions ofmarkers known to be indicative of signs or symptoms of a disease. As newscientific research and medical research becomes available, and moredata about different types of patients is obtained, the computer system110 can fine-tune the set of markers, adding new markers for datacombinations that indicate signs of the disease, modifying existingmarker definitions to be more accurate in predicting signs or symptomsof a disease, and removing marker definitions that are not sufficientlyeffective. The user device 104 a, using an application that uses thedata in the module 112, can receive and process the marker definitions,as well as assess incoming data streams and detect and record when thecriteria for detecting a marker is satisfied.

For example, certain measurements or detected items (e.g., bodytemperature above a threshold, coughing frequency above a threshold,etc.), whether reported by the user 102 a or detected through passivesensing, may be designated as predetermined markers or indicators that,while not conclusively showing that a person is infected with COVID-19,nevertheless indicate increased probability of infection. Markers orindicators may be more complex, involving evaluation with respect to apersonalized references, involving combinations of measurement types ordata types, having conditional applicability, and other features. Forexample, a marker may be based on the user's personal baseline, e.g.,with criteria for a marker being present as a change of at least acertain magnitude or degree from a corresponding personal baselinemeasure for the user 102 a or from a previous measurement for the user102 a. Criteria for a marker may be involve multiple differentmeasurements or data items that must each meet a correspondingrequirement (e.g., value in a predetermined range, an amount ordirection of change relative to a reference value, etc.) at or near thesame time (e.g., within a threshold amount of time of each other). As anexample, a marker could have criteria that requires three measures, bodytemperature, blood pressure, and resting heart rate, to each be measuredas having a value in a respective reference range at least once during a4-hour period. Similarly a marker might require two out of threedifferent conditions to be met in order for the marker to be consideredpresent. Similarly, a marker may have conditional properties. A markermay have criteria that involve a certain data measure (e.g., abehavioral or physiological measurement) occurring in a predeterminedcontext or set of contexts. For example, a marker may be defined aselevated resting heart rate (e.g., relative to an absolute referencelevel or a person's personal baseline level), but only if measured atcertain times of day or during certain activities. A data item may beconsidered to occur in a certain context (e.g., during a certainactivity, time, location, condition, etc.) when the measurement orcondition represented by the data item occurs within a predeterminedlevel of proximity in time and/or location of the context.

The module 112 may include risk assessment components, such aspredictive models, which can be refined through machine learning. Forexample, the computer system 110 can generate and train machine learningmodels 612 based on the data collected about many users, which canindicate changing behavioral and physiological measures of patients,behavior patterns and corresponding exposure and infection, vaccinationresponses, side-effects of drug delivery, and more. Models 612 can beprovided to the user device 114 a as part of the module 112, as updatesto the module 112, or through other means. In addition, or as analternative, the models 612 may reside on a server, such as the computersystem 110, and be used there to evaluate data that is received. Themodels 612 can provide outputs that are based on a diverse set of datasources. The outputs of the models can serve as inputs for scoring thelikelihood of disease outcomes and risks.

The computer system 110 can include features to detect and identifyneeded steps to improve detection and prediction. For example, thecomputer system 110 can identify the absence of data for certain factorsand initiate actions to collect the needed data. If the computer system110 determines that there is a low confidence score for a prediction forthe user 102 a, the computer system can take actions to improve theconfidence level. As an example, the computer system 110 can identify ameasurement that is relevant to the current prediction and for whichdata is inadequate (e.g., missing, outdated, outside a standard orexpected range, etc.). The computer system 110 can then cause datacollection procedures to be changed to acquire or confirm themeasurement needed. If the data needed is exercise data, for example,the computer system 110 may send a message to the user device 104 acausing the user device 104 a to present an EMA or survey asking theuser to input the exercise data needed. In addition, or as analternative, the computer system 110 can instruct the user device 104 ato turn on exercise monitoring, to report stored exercise data, tocollect and report exercise data from an activity tracker device, and soon. In some cases, the computer system take action to obtain data orconfirm data received through administered tests, conditional effects oftreatment changes related to medication delivery, suppressionmechanisms, and behavioral or environmental adjustments.

Based on the outputs of the machine learning models 612, the computersystem 110 can determine an action to change a disease detectionprocedure for the user 102 a or change a disease treatment for the user102 a. For example, changing the disease detection procedure can includechanging one or more of: the types of data monitored; the types ofsituations or conditions in which data is acquired (e.g., adding orremoving triggers for collecting sensor data or triggering presentationof an EMA); the types of devices used for monitoring; the frequency ofdata collection; the precision or format of data collection; adding alaboratory test (e.g., blood test, urine test, etc.); adding a test fora specific disease (e.g., a test to detect COVID-19, whether to detectantibodies or current infection); and so on. This can include selectinga detection component, such as a treatment kit to be sent to the user102 a. Changes in disease treatment can be varied and are highlypersonalized for the user. Changing a treatment can include maydifferent types of actions, and a few examples include providing avaccine, providing a medication, providing digital therapeuticsinterventions, changing settings of a medical treatment device (e.g.,ventilator) or a medical monitoring device, and so on. Once selected,changes to monitoring or treatment can be recommended to the user 102 a,the user's healthcare provider, and/or a caretaker for the user 102 a,and in some cases can be carried out by the computer system 110.

The models 612 can be neural networks or classifiers. Other types ofmodels that may be used include support vector machines, regressionmodels, reinforcement learning models, clustering models, decisiontrees, random forest models, genetic algorithms, Bayesian models, andGaussian mixture models. Different types of models can be used togetheras an ensemble or for making different types of predictions. Other typesof models can be used, even if they are not of the machine learningtype. For example, statistical models and rule-based models can be used.

In the example of FIG. 6, the computer system 110 includes a collectingagent 620, an exposure risk agent 630, a predictive agent 640, a scoringand classification agent 650, and a delivery agent 660.

The collecting agent 620 obtains and processes data about the user 102a, e.g., EMA data 602, sensor data 604, and treatment data 604, as wellas data potentially from any other systems containing patient healthrecords. The collecting agent 620 can format and store the data in adatabase 622, where historical data about the user 102 a is maintainedas well. The database 622 can include various other types of informationabout the user 102 a, such as race, gender, age, and other userattributes. The database 622 can make the collected data, as well asdata derived from it, such as personal baseline measures for the user102 a, available to any or all of the other components of the computersystem 110.

The exposure risk agent 630 accesses and uses geographic informationsystems (GIS) at multiple levels, e.g., neighborhood, community, city,county, state, regional, and country levels. The exposure risk agent 630combines this data with collected user data through contact tracing,exposure identification measures, and treatment response indicators todetermine hotspots, potential outbreaks and re-emergences of risksassociated directly with a disease, indirectly due to the relatedsuppression mechanism or the treatment of a virus. This information isdelivered to the collecting agent 620 in order to synchronize relevantevents. For example, the collecting agent 620 can align or associateactions of the user 102 a and input of the user 102 a, as well ascontext data indicating the times, locations, and activities of theactions and inputs, with the disease corresponding disease prevalencemeasures. If a user visits a restaurant, for example, the collectingagent can detect the visit from GPS location data, log the amount oftime the visit lasted, and associate the visit with an occupancy levelat the restaurant (e.g., a value which may be typical, average, recent,or expected value if the actual value is not available) and the diseaseinfection rate in the area of the restaurant, so that the exposure riskto the user 102 a of that particular visit to that particular restaurantat that particular day and time can be estimated.

The predictive agent 640 uses predictive models 612 to estimate theexposure of the user 102 a and the likelihood of infection by the user102 a. The predictive models 612 can be trained based on training dataof many different users and situations, and the models 612 can berepeatedly or continually updated as addition information comes in. Thepredictive agent 640 predicts an exposure level based on, for example,the community exposure data (e.g., infection rates and trends for areasthe user 102 a has been over a period of time, such as the last fewdays, the last 1-2 weeks, etc.) and the tracked data about the user(e.g., locations the user 102 a has been, frequency of travel, amount oftime spent outside the home, types of locations visited, contact tracingdata, and so on). This can provide a measure of exposure, such as apredicted likelihood that the user 102 a has been exposed to a disease,a frequency or intensity with which the user 102 a has been exposed, alikelihood that the user will be exposed in the future (e.g., over aparticular time period in the future, such as the next day, week, month,or year) estimated based on the user's recent and long-term behaviorpatterns as well as community exposure measures, a general exposurescore or exposure level classification based on various exposurefactors, and so on.

The predictive agent 640 can also predict whether the user 102 a iscurrently infected or, in some cases, whether the user 102 a haspreviously been infected. To do this, the predictive agent 640 can usethe collected data from various sources (e.g., health records 606, userinput such as EMA data 602, sensor data 604, treatment data 608, userdemographic data, and so on), using collected data collected for acurrent time period (e.g., the current day, week, etc.) as well ascollected data for prior time periods and/or long-term historical data.These data items may be input to a model or analysis process in any ofvarious ways, e.g., as measurement values (e.g., a body temperaturemeasurement of) 99.0°, as classifications of measurements or datapatterns (e.g., activity level is high, medium, or low), as binaryfeature values (e.g., whether an event or condition was detected ornot), as a count of how many times an event or behavior occurred, as anaggregate measure (e.g., an average, maximum, minimum, etc.) based onmultiple observations, and so on. Detected markers for signs or symptomsof a disease can be indicated and used as input also, for example, as aresult of pre-processing the collected data set for a user and comparingcollected data patterns with criteria of marker definitions to see whichmarkers are reflected in the collected data. Baseline measures for auser, demographic attributes of the user, and other types of data canalso be provided as input to the infection likelihood determination.

The models 612 used to predict infection can take into account theexposure estimates for the user. For example, a score or other output ofan exposure estimation model may be used as an input to the infectionprediction model. In addition, or as alternative, the activities of auser and locations a user has been, as well as the infection rates inthe user's community, can be used to determine the likelihood ofinfection. For example, an elevated body temperature may generally be asign of potential COVID-19 infection. But if the user's community has avery low prevalence of COVID-19 and/or the user's activities indicatevery low contact with others (e.g., based on location tracking of theuser's phone and smartwatch, from user-reported survey answers, and/orcontact tracing data), the predictive agent 640 may interpret thelikelihood of infection to be lower than it would otherwise be forsomeone with more travel or someone in an area with higher prevalence ofthe disease. Thus, at least in some implementations, the computer system110 can weight the detected signs and symptoms of a disease according tothe community data related to the disease. In some implementations, thepredictive models 612 for predicting infection likelihood and/or otheritems may receive indicators of community characteristics and communityexposure levels, and the training of the models 612 may automaticallyaccount for the variation in the predicted item due to factors such asuser locations, user activities, user exposure level, communitycharacteristics, community exposure levels, etc. In other words, thepredictive models 612 can be trained to receive and process inputfeature values indicative of any of these factors, and potentially anyor all of the data items collected for the user. Through the modeltraining process, the model 612 learns the relative importance andpredictive value of each type of input, as well as how differentcombinations of input values increase or decrease likelihoods, so thatthe trained models 612 automatically generate a prediction using therelationships implicitly learned through machine learning training froma training data set showing many examples of other users.

The predictive agent 640 may be used to predict other items as well. Forexample, the predictive agent 640 can include a model 612 trained topredict, based on information about the user 102 a, a susceptibility ofthe user 102 a to COVID-19 if the user were to contract the disease. Forexample, separate from the user's exposure level, the susceptibility maybe an estimate or predicted measure of how vulnerable the user would bewould be to the disease (e.g., the degree that the user may be affected)if the user were to be exposed to the disease and/or if the user were tobecome infected. The susceptibility measure could be, for example,indicative of a risk category (e.g., high, medium, or low risk of acertain level of harm occurring). The susceptibility measure could be aprediction of a likelihood that a user, if infected, would have asymptomatic case rather than an asymptomatic case. The susceptibilityprediction may be made in general (e.g., a level of severity for diseaseeffects as a whole), or with respect to specific outcomes or harms ofthe disease (e.g., a prediction of risk of stroke, a prediction of riskof hospitalization needed, a risk of sepsis, a risk of death, etc.). Aswith other predictions of the predictive agent 640, all of thesepredictions of a user's susceptibility to the disease can be based onrelationships implicitly learned or trained into a model 612, such as aneural network, based on training data describing data of other users,e.g., data collected for users of different types and characteristicswho experienced the disease and the disease outcomes they experienced.The models 612 can also be repeatedly or continually updated withfurther training and refinement as additional training data examples areobtained. To determine a user's susceptibility to the disease, theappropriate model 612 may use (e.g., receive inputs indicating) varioustypes of data about a user, such as a user's demographic characteristics(e.g., age, sex, etc.), the user's physiological data (e.g., bloodpressure, height, weight, respiration rate, resting heart rate, etc.),the user's behavioral data (e.g., current and historical records forexercise, sleep, nutrition, the user's medical records (e.g., dataindicating comorbidities and chronic conditions, such as diabetes, heartdisease, lung disease, COPD, cancer, etc.), and so on.

As another example, the predictive agent 640 may generate estimates of auser contracting COVID-19 based on the user's behavior patterns andcommunity exposure levels. For example, the collected data about a user,including the locations visited, activities performed, and so on, canindicate behavior patterns and behavior trends of the user. Thepredictive agent 640 can thus use the collected data, including anydetermined patterns or trends, to predict a likelihood that the userwould become infected over a future time period, such as the next day,week, month, year, etc. if the current patterns or trends continue.Similarly, the prediction can take into account the patterns and trendsof infection in the user's community, and use those in making theprediction. For example, a model 612 can be trained to make this type ofprediction based on training data examples that indicate trackedbehaviors of different individuals, along with their community exposuremeasures and outcomes of whether the individuals contracted COVID-19 andwhen.

The scoring and classification agent 650 takes the collected inputelements (e.g., records and measures of what has occurred, such asphysiological readings, user actions and behavior patterns, etc.) andcombines them with the predictive input elements and determining overallrisks associated, and what recommendations can be shared with thedelivery agent. The output of the predictive agent 640 may be providedto the scoring and classification agent 650 to be factored into theprocessing of the scoring and classification agent 650. Based on thepredictions of the predictive agent 640 and potentially other data, thescoring and classification agent 650 can generate scores andclassifications to be indicated to the user 102 a, a physician, acaregiver, community health teams, and so on. For example, based on thepredicted likelihood of infection the agent 650 may determine adiagnosis of whether the user 102 a currently is infected with COVID-19or not. This may be involve assigning a score or classification for theuser 102 a based on where the predicted likelihood of infection fallswith respect to various ranges of predictions, e.g., unlikely to beinfected (0%-20%), low likelihood of infection (20-30%), moderatelikelihood of infection (30%-50%), high likelihood of infection(50%-80%), diagnosis of actual infection (e.g., 80%-100%). Similarly,exposure levels, infection likelihood, and other items may be scored orclassified for any appropriate scale or set of classes.

The delivery agent 660 uses outputs of the scoring and classificationagent 650 to select, notify about, and carry out actions with respect toCOVID-19. The delivery agent 660 can act on the scores andclassifications indicated by the scoring and classification agent 650 todecide to change monitoring, notify the user 102 a or an associatedperson (e.g., physician, caregiver, etc.), provide or change treatment,and so on. As options for managing the disease, the delivery agent 660can consider actions such as the ordering of or registration of a userfor laboratory tests (e.g., blood test, urine test, etc.), sending theuser a disease testing kit to detect the disease, providing a vaccinefor the disease, providing or using a certain type of device (e.g., forproviding treatment or for improving monitoring), changes in theconfiguration or software of the user device 104 a, changes in datacollection procedures of the user device 104 a (e.g., types of datamonitored, frequency of collection and reporting, etc.), and adding orchanging medications for the user. The actions that the delivery agent660 selects can be identified and communicated to the user 104 a throughan appropriate communication medium, e.g., email, phone call, shortmessage service (SMS) text message, or paper mail delivery. The deliveryagent 660 can also connect to centralized registries to report actionstaken for the user 102, to allow continued identification of communityhotspots, outbreaks, and risks related to the disease and its treatment.

In some implementations, the delivery agent 660 can providerecommendations to titrate, adjust, or replace elements of treatment orother management of the disease. Titration can involve repeatedly (e.g.,in some cases continually or continuously) measuring and adjusting the adrug dosage, digital therapeutic intervention, or other aspect oftreatment. The delivery agent 660, having information about recentcollected data for the user 102 a, as well as the health records 606 andtreatment data 608 for the user 102 a, can recommend varying medication,sleep aids, over-the-counter solutions, intensity or type of digitalinterventions (e.g., interactions for behavioral modification orbehavioral support) related to treatment for the disease. Therecommendations for changes in treatment can be communicated to the user102 a (e.g., through the device 104 a) and/or to device of a physician,a caretaker, a family member, or other person associated with the user102 a. In some cases, the recommended actions, along with thepredictions, scores, and classifications of the other agents 640, 650for the user 104 a, can be provided as a form of decision support to aphysician, to indicate a recommended risk level, diagnosis, prognosis,and treatment for the user 102 a, which can inform the decision makingof the physician in caring for the user 102 a. The delivery agentrecords decisions for the user 102 a into the health records 606 for theuser 102 within the system 110, and can allow access to externalsystems.

In some implementations, the delivery agent 660 does more than simplyselect, store, and notify of personalized actions selected to manage thedisease for the user 102 a. For example, the delivery agent 660 canimplement disease management actions by causing actions selected for theuser 102 a to be carried out. This may be done automatically, forexample, when the delivery agent 660 has at least a minimum level ofconfidence that an action is appropriate for the user 102 a, and/or whenrules or conditions for carrying out the action are satisfied. In someimplementations, carrying out the actions may be predicated onconfirmation or approval from another source, such as an approval oracceptance by the user 102 a through the user device 104 a, approval ofa physician for the user 102 a, approval of an insurance company for theuser 102 a, etc. To obtain this approval, the delivery agent 660 canprovide data prompting a device to present a user interface requestingconfirmation of an action to be performed.

The actions performed by the delivery agent 660 can include causing anitem to be delivered to the user 102 a, for example, a disease testingkit, a medical monitoring device, a laboratory test sample collectionkit, an at-home laboratory testing kit, a medication, etc. The deliveryagent 660 may carry this out by communicating with an fulfillment systemto initiate ordering and/or shipment of the items that it selects. Theactions performed by the delivery agent 660 can include changingsettings of the user device 104 a, the wearable device 107 a, themedical monitoring device 108 a, or other devices.

Although the remote computer system 110 cannot actually administer avaccine to a user, the delivery agent 660 may nevertheless facilitateadministration of the vaccine by, for example, informing the user 104 aof the selected vaccine and locations to obtain it, scheduling anappointment for the user to receive the vaccine, prescribing the vaccineor indicating the vaccine recommendation in medical records of the use102 a, initiating communication to prompt the user's physician toprescribe the vaccine for the user 102 a, causing an appropriate typeand dose of vaccine to be ordered and shipped, and so on. Similaractions can be performed for medications and other types of treatment.

The disease management actions that the delivery agent 660 selects amongand carries out can include providing digital therapeutics to manageCOVID-19. This can include providing interactions to prevent, detect,monitor, treat, or support recovery from the disease. The delivery agent660 can initiate delivery of new digital therapeutics through the userdevice 104 a and/or other devices, such as devices wirelessly connectedto the user device 104 a. The delivery agent 660 may cause new digitaltherapeutics programs or treatment plans to be started. In someimplementations, the delivery agent 660 may adjust digital therapeuticsprograms or treatment plans that are already being used. For example,the delivery agent 660 may adjust the type, frequency, intensity, ortype of digital therapeutics, or adjust conditions in which they areprovided, e.g., contexts in which they are administered, triggers foradministration, and so on. The digital therapeutics may instruct a userto take actions, refrain from actions, or change behaviors in order toreduce or avoid symptoms of the disease or to speed recovery from thedisease. As an example, if a COVID-19 patient is having light ormoderate difficulty breathing, digital therapeutics can be provided torecommend that the user change the pose of their body, for example, tolie on his stomach rather than on his back. Similarly, digitaltherapeutics may instruct changes to a level of physical activity orspecific exercises (e.g., recommending more rest or recommending agradual increase in physical activity, depending on the stage of thedisease and the user's capabilities).

The computer system 110 can cause digital therapeutics to be deliveriesin various ways. The computer system 110 can send modules 112 or updatesto modules 122, where the modules 112 configure a user device 104 a(e.g., through an application resident on and able to be executed by theuser device 104 a) to locally detect conditions and triggers and provideinteractions (e.g., notifications, surveys, games, media, userinterfaces, education, etc.) that the user device 104 a selects asappropriate for the detected situation of the user 102 a. As anotherexample, the computer system 110 can assess the collected data for theuser 102 a and initiate specific interactions. For example, the computersystem 110 can select specific interactions and the timing of thoseinteractions based on the collected data, and then send messages to theuser device 104 a causing the user device 104 a to initiate theinteractions with the user 102 a.

The computer system 110 can be used to monitor the effectiveness oftreatment of the user 102 a and adjust treatment accordingly. Forexample, if provided digital therapeutics do not result in the expectedor desired improvements in physiological attributes or user behaviors,the computer system 110 can select and provide different digitaltherapeutics interventions. As another example, if medications provideddo not yield the desired effects, or if the collected data indicatesthat there are problematic side effects, the computer system 110 canrecommend changes to the medication regimen, such as changing the dose,type of medication, frequency or timing of administration, and so on. Inmaking treatment decisions and recommendations, the computer system 110can use data indicating medical research results and best practices, forexample, to provide actions based on clinically validated andevidence-based treatment steps that can be captured in rules, look-uptables, databases, or other data structures.

In addition, or as an alternative, the computer system 110 can derivetreatment recommendations from a database of example cases of thedisease. That data may describe instances of people who have had thedisease, the attributes and monitored data for those people, theirsymptoms and indicators of disease, treatments provided, and theresponse of the people to the treatments. From analysis of theseexamples, the computer system 110 can derive rules and relationshipsthat indicate, for example, the likelihood that certain treatmentelements improve different symptoms, and with what magnitude and speedof improvement is typical. The analysis can take into account thatdifferent individuals have different characteristics and situations andso may respond differently. Thus, the result may be a set of rules orother data that indicate, for different disease symptoms, usercharacteristics, and user behaviors, the corresponding treatment actionsthat are predicted to have the greatest benefit to the user (e.g., mostlikely to improve symptoms or speed recovery, providing the greatestbenefits, providing the lowest risks or side effects, or somecombination of these or other criteria). The training data can be usedto train machine learning models to predict the treatment actions torecommend and carry out, where the machine learning models can receiveinput of a user's demographic data or other characteristics, the user'sphysiological data, and the user's behavioral data, and then provideoutput indicating treatment options that the model predicts are mostappropriate for that combination of input user data.

FIG. 7 shows an example process 700 for tracking signs and symptoms ofdisease and generating a treatment plan for a patient. This generalprocess 700 can be used for detecting and providing insight for variousdifferent situations, including scenarios such as: (1) detecting signsand symptoms of viral infection worsening or Improving; (2) detectingsigns of vaccines being effective or ineffective; and (3) detectingsigns of morbidities and co-morbidities.

As discussed above, a user is provide with a digital treatment package,such as one of the modules 112 discussed above. The user has severalsigns and symptoms that require monitoring for associated risks due toan illness worsening or improving. Further, there are additional risksor changes to treatment possibly required based on morbidities,co-morbidities, and other related illnesses and the treatment of these.

The example of FIG. 7 shows that a portion of a treatment plan involvesidentifying signs and symptoms of a disease 710, which can be used todetect infection and/or to monitor the progression of a disease. Atreatment package or module 112, and/or processing of the computersystem 110, can be designed for monitoring with respect to a specificdisease or set of diseases, and so can be configured to detect andcharacterize (e.g., determine the frequency, severity, etc.) a specificset of conditions determined to be signs or symptoms of one or morediseases. In the example, this includes fever 712 and difficultybreathing 714 as signs of COVID-19 infection. Other symptoms 716 canalso be monitored, for example, to detect general behavior changes andphysiological changes that may indicate other symptoms.

For each of various types of signs or symptoms, there may be multipleelements, e.g., measurements, types of data, or tracked behaviors, thatindicate or confirm that a the sign or symptom is present. For example,to detect a fever, the system can detect an elevated temperature, butmay also detect signs like chills, perspiration, fatigue or changes incognition that may be related and may be used to confirm or verify thepresence of a fever. Similarly, difficulty breathing may be detectedwith respiration rate, heart rate variability, and/or reduction inoxygen, with these measured parameters being compared with baselinevalues, thresholds, or other reference values to identify difficultybreathing. Other symptoms may be detected using device data (e.g.,sensor data, device usage data, etc.) and results from various tests,such as taste and smell tests, blood tests, urine tests, swabcollections, and so on. The data sets that can be used include activesensing data, passive sensing data, user inputs (such as user responsesto surveys and EMAs, EHR/EMR data, clinical data set, and insuranceclaims data.

For a disease like COVID-19, the signs and symptoms can be verydifferent for different individuals. Some include loss of taste andsmell, coughing, difficulty breathing, fatigue, fever, gastrointestinaldistress, confusion or disorientation, skin rash in toes or extremities,blood clots, stroke, and more. The system performs risk scoring 720 thattakes into account the prevalence of different symptoms for differentindividuals, to better predict the likelihood of infection. For example,fever is a sign of COVID-19 infection, but can also be a sign of otherillnesses or conditions. The combination of different signs andsymptoms, as well as the data collected that may represent markers orindicators for the disease, can all be used together to determine anoverall likelihood of infection.

The system then generates a treatment plan 730 based on the estimatedlevel of risk and likelihood of infection of the person. This caninclude selecting and initiating a testing or treatment action based onthe distributed monitoring techniques discussed above.

For instance, an individual with respiratory illnesses, such as chronicobstructive pulmonary disorder (COPD), may have reduced pathways forbreathing and increased risk in delivering oxygen to vital organs. Whencombined with an illness like COVID-19, which creates additional risksto breathing, it becomes increasingly important to measure the health ofthe body under varying situations. These can include measuring theuser's capabilities, actions, and physiological attributes when exertingenergy while standing, sitting, walking, running, talking, and managingcognition during events like driving. Similarly, it is helpful tomeasure how well the user recovers energy or resumes normal breathingwhile resting, relaxing, sleeping, or meditating. Unlike some systemsthat are limited in the situations or activities in which monitoringdata can be effectively used, the system described herein can gather anduse information about each of these different actions or contexts, andthis diversity is a significant advantage in obtaining accurate results.By monitoring changes in physiological and behavioral measures duringdifferent activities that place different types or levels of stress onthe user, as those activities take place naturally from day to day andwithout prompting by the system, the system can better characterize anddetect a user's risk, early signs of a disease, and the effects of adisease and treatment. In many cases, this variety of monitoredsituations and the ability to detect changes with respect to personalbaselines for the different activities can lead to earlier and moreaccurate diagnoses and better treatment selection decisions.

In the case of a person with COPD, due to increased risk when person isexposed to or contracts COVID-19, the system can select to recommend orrequire a pulse oximeter as a trusted source for monitoring, in order toprovide more accurate data than, for example, self-reported indicatorsof breathing difficulty. This step may be made before infection isdetermined, e.g., to better detect potential future infection, or afterinfection is predicted to have occurred or has been confirmed throughlaboratory tests, or even after the infection is cleared as a person isdealing with lingering effects of the disease.

As another example, the risk of loss of life while sleeping may besignificant for some users that have reduced lung function or conditionssuch as sleep apnea, when they are unable to manage their ability tobreath due to the inability to consciously control the increased demandfor air flow. Given this risk, the system can provide recommendationsand behavioral support to manage the risk. For example, the system canprovide recommendations to eliminate all sleep aids in many cases. Thenegative effect of the elimination of sleep aids may inadvertentlyincrease the risk for apnea events if the patient also suffers from achronic co-morbidity of chronic insomnia. In many cases, patients thathave apnea events tend to suffer most during periods of light sleep.Where a sleep aid may help ensure periods of deep sleep, the likelihoodfor dying during sleep may be more likely during periods of light sleepwhich along with REM sleep is more common for insomniacs.

In some cases, in co-morbidity situation, a ventilator may be the onlycourse of action combined with the original COVID-19 treatment packageand any pre-existing co-morbidity treatment packages.

FIG. 8 shows additional examples of signs and symptoms of a disease suchas COVID-19 and how they can be used. Signs 810 can includephysiological measures such as the presence of a fever, increasedresting respiration rate, and so on. Symptoms 820 can includephysiological effects such as headache, nasal symptoms, throat ache ordryness, coughing, stress, dyspnea, nausea, diarrhea, myalgia, loss oftaste or smell, and so on. Although not listed, behavioral signs orsymptoms can also be determined, such as confusion, reduced energy oractivity, reduced social engagement, change in types of activities,changes in sleep patterns, and so on. Many of these signs and symptomsmay occur individually for different reasons, and so the systemevaluates the combinations of detected items and their timing related toeach other to evaluate likelihood of infection. Similarly, the systemconsiders the severity or intensity of the signs and symptoms, forexample, through qualitative indicators from a user, measures offrequency that events such as coughing are detected, the degree to whichmeasurements or behaviors are changed and so on. Signs and symptoms canbe detected using a variety of information, such as passive sensing,active sensing, self-reported data, EMR/EHR data, treatment data,insurance claims data, and so on.

In general, when using a downloaded treatment package or module 112 auser can perform a series of events based on content delivered to theuser and data collected for the user. The client device 104 a, based onthe downloaded treatment package and/or communications from a serversuch as the computer system 110, provides advice and interactions to theuser 102 a based on collected results. Treatment packages can providevarious services discussed below. Examples include interactions toperform treatment check-ins, processing to determine containment andexposure risks, and measurement of side effects of treatment. These canbe performed using self-reported data, for example, responses tomedication reminder messages, questions whether the user has any troublebreathing, questions about whether the user has experienced loss oftaste and smell, etc. The system can also use device data, e.g. sensordata indicating behaviors and physiological measures for monitoring forreal-time stress indications. The system can also use health recordsdata, e.g., to generate an initial baseline using prior measurements.This could be used, for example, when considering whether blood pressuremeasurements are higher or lower than the baseline either in in generalto detect infection, or after a treatment action (such as receiving avaccine or a medication), etc. The system can also use disease exposuredata for both the individual and the community, e.g., communityinfection level information, contact tracing, data indicating diseasehotspots, and so on.

Other functions of the downloaded treatment package or module 112 and/orcomputer system 110 include adjustments to monitoring and diseasedetection, including potentially ordering laboratory test materials ordisease testing kits. The module 112 and/or computer system 110 candetermine when additional devices, such as a pulse oximeter,thermometer, sleep sensor, etc., is needed, whether to detect infectionwith greater certainty or to monitor treatment. Similarly, the systemcan determine disease test kit needs, such as to initiate delivery ofat-home testing kits. The system can also determine laboratory testingneeds and can schedule laboratory tests (e.g., specimen collection,blood testing, urine testing, etc.)

Other functions of the downloaded treatment package or module 112 and/orcomputer system 110 include providing changes to treatment and providingadvice for changes in user behaviors. These include activities to detectimpairment of cognition and to improve cognition. Some can include restand relaxation activities, e.g., puzzles, games, non-digitalrecommendations, reading material, etc. These can be used to both helpcalm a user and improve mood, as well as provide an activity in which tomonitor the user's responses and performance. The system can providesleeping guidance, including contraindications to sleep medications andsleep aids. The system can instruct the user to, for example, takebreaks, to stand up from time to time, to move rather than staysedentary, and so on. The system can provide periodic check-ininteractions to assess mood, pain level, or generally ask how the userfeels. The system can monitor calorie consumption needs and provideeating reminders, as well as recommend changes to improve nutrition toprovide meal options and ideas. The system can provide exerciserecommendations, e.g., programs for stretching, aerobic exercises, stepactivities, strength training, etc. The system can also providehydration reminders and recommendations.

Other functions of the downloaded treatment package or module 112 and/orcomputer system 110 include changing medication, such as adjustments tothe type, quantity, frequency, or combination of mediation taken. Thesystem can also make titration changes, periodically check forcontraindications, determine the need for medication refills, and so on.

Other functions of the downloaded treatment package or module 112 and/orcomputer system 110 include providing social modifiers to managing thenegative impacts of disease suppression mechanisms like socialisolation. The system can recommend or initiate communication withsomeone, e.g., through a phone call or video conference call. The systemcan monitor speech patterns to identify speech pattern differences,which may be a sign of cognitive impairment, extreme fatigue, or otherpotential disease symptoms.

In some implementations the system uses collaborative assessments todetermine impacts and change in a user. For example, the system mayinitiate communication with a device associated with a friend or familymember of the user 102 a, and ask if the friend or family member hasnoticed changes in the user 102 a, and if so, the type and extent ofchanges. This can provide confirmation for signs or symptoms of diseasethat may be indicated in other collected data or may reveal additionalsigns or symptoms.

Additionally, the system can use using passive sensing to determine riskand recovery. For example, within the realm of social isolation apatient or participant in a treatment package is determined to managesuppression mechanism like social isolation. The patient or participantis at risk for mental health related depression symptoms based on priorepisodes but has been managing using a specific drug. While sociallyisolated, the increased symptom clustering insight detects higher thannormal situations related to depression. Ultimately causing an increasedtitration or dosage of medication to the patient. This detection throughsymptom clusters utilizes multiple sensors within these clusters. Onesymptom cluster is a physical activity decrease, which the system candetect through accelerometer data, step counting data, motion detection,and so on. Another is changes in food intake, such as changes in caloricintake indicated through self-reported responses to EMAs or other userinterfaces, food diaries, etc. Another cluster is reduction in socialactivity, which can be detected through records of phone call durationand number of calls, records of social application usage, and so on.Another example is sleep-related changes, such as duration and qualityof sleep, which can be detected through phone and application usage(e.g., detecting when a phone or app is used and when it is not used),light sensor data, and so on. Another example, is mood changes, whichcan be determined based on self-reported data, physical activity sensordata, heart rate and heart rate variability data, which can indicatestress and other symptoms, and so on.

FIGS. 9A-9B show examples of user interfaces that may be providedthrough a disease management data package, e.g., one of the modules 112,at a client device. The client device 104 a can include a client-sidedelivery component, configured to provide interactions with the user 102a to cause data gathering to detect or monitor COVID-19, as well as tomonitor the administration and effects of treatment of the disease andeven to provide digital therapeutics to treat the disease (e.g., reducesymptoms, improve mental state and physiological function, etc.).

FIG. 9A shows an example user interface 900, presented on the userdevice 104 a, which indicates results of processing collected data forthe user 102 a. The system has determined that the user has a highexposure risk to COVID-19 and indicates this to the user. The systemalso has determined a significant change with respect to the user'spersonal baseline for sleep. In this example, the user's amount of deepsleep and total sleep has increased significantly, which is potentiallya sign of exhaustion or fatigue. As a result, the system recommends thatthe user should not take any nighttime sleeping aids, as these wouldpose an undue risk to the user 102 a in the current situation. The userinterface 900 also includes controls for a user to begin furtherinteractions to acquire information about other topics.

FIG. 9B shows another example user interface 950 that shows differentquestions and corresponding answers from the user 102 a, along withscore weightings for the user's answers that together result in asignificant level of risk of contracting COVID-19, and so the systemdetermines to send the user 102 a a testing kit to test for the disease.For example, the score weights for each answer can be added and comparedto a threshold, and when score satisfies the threshold, the systemdetermines to send a testing kit for the disease.

FIG. 10 provides examples of digital therapeutic delivery as a suite ofexemplary tools or components. These tools can be used to providetreatment for COVID-19 or other diseases. The example shows a sensornetwork, e.g., a set of different devices and sensors that cancommunicate with the user device 104 a to provide information about theuser 102 a. The information from these sensors can be used by the userdevice 104 a and the computer system 110 to provide identifiedtreatments, programs, engagements, and interventions. Activities andphysiological attributes of the user 102 a are detected using varioussensors, which provide sensor data through the user device 104 a oranother personal network. The computer system 110 communicates with theuser device 104 a, or alternatively other devices, to receive sensordata and to distribute therapeutic tools capable of managingpreventative, predictive, treatment and post-treatment needs of the user102 a. A few examples of tools and digital therapeutics that can beprovided include interventions to provide posture training and support,breathing training and support, mood and mental health assessment andsupport, and exercise training and support.

A variety of personalized digital interventions can be provided forusers. The user device 104 a and the computer system 110 can communicateto perform in-the-moment monitoring to promote active engagement withthe user. The user device 104 a, by its own local processing or inresponse to instructions from the computer system 110 over a network,can provide instructions or cues on screen to describe what the usershould do. For example, the user device 104 a can instruct the user toassume certain poses or perform certain physical exercises, and monitorphysiological measures while the user takes the instructed actions. Theactions can also be timed or otherwise monitored. From the capturedinformation, the system can update the user's baseline measures,classify the user's performance, and select an appropriate type andintensity of digital therapeutics to deliver in the future. Thetechniques can be used throughout the entire disease management cycle,for detection of or prediction of infection, treatment of the disease,and supporting long-term recovery from the disease (e.g., monitoring andimproving function after the infection has cleared).

The selection and delivery of which interventions to provided, and thetiming of when to provide them can determined based on the personalbaseline measures for the users, current monitoring data for the users,and trained machine learning models. For example, as discussed below,posture training and support can be provided as a therapeutic tool toaddress COVID-19 symptoms or to speed recovery. Machine learninganalysis can be performed using monitoring data showing postures overtime of different patients who have contracted COVID-19 andcorresponding breathing ability and recovery over time. The data canprovide monitored data and results for individuals of differentattributes, backgrounds, and situations. The data can show monitoredresults after interventions as well as results from postures thatpatients choose on their own without prompting. Similar information suchas breathing patterns, exercise patterns, and so on can be monitored andassessed using machine learning techniques.

As an example, the computer system 110 and the user device 104 a canprovide digital therapeutics to monitor and improve the body posture ofthe user 102 a. Many recovering individuals spend time laying down,whether in a hospital or at home, and this can lead to additionalmedical problems and well as overall physical fitness deconditioning. Bymeasuring posture regularly throughout the day, both the amount of timesitting and standing and lying down can be collected and comparedagainst reference values (e.g., thresholds, ranges, etc.) that specifydesirable levels for treatment of disease and providing effectivepost-treatment recovery. For example, treatment of COVID-19 may involvechanging posture such as: a user varying posture with at least a minimumfrequency; a user spending a majority of time or at least a minimumamount of time in certain desirable postures; minimizing time in oravoiding undesirable postures; spending desired amounts of time orportions of the data in specific desired postures; and so on.

Posture can be measured in a variety of ways. In some cases, the sensorsof a phone or smart watch that sense direction and movement (e.g.,accelerometers, IMUs, etc.) can be used to estimate posture or changesin posture. Posture can also be measured using an instrumented patch ora garment, for example, wearable sensors like UPRIGHT GO and LUMO LIFTcan effectively detect a position of the wearer, and can provide thedetected position of the individual to a smartphone, e.g., using awireless communication link such as BLUETOOTH.

The computer system 110 and/or user device 104 a can provide variousinteractions to deliver posture training and feedback to the user. Theseinclude educational materials, media, reminders, alerts (e.g., triggeredwhen an undesirable posture is detected or when an appropriate postureis received), assessments, surveys, games, and so on. Theseinterventions can be triggered in a variety of ways. For example,posture training and support may be prescribed by a doctor for the user102 a. As another example, the computer system 110 and/or 104 a maydetermine to provide posture training and support to the user 102 aautomatically upon detecting one or more conditions, such as determiningthat resting respiration rate has increased above a threshold, thelikelihood of infection exceeds a threshold level, and/or the user'sposture as tracked using the user device 104 a or another device failsto meet certain standards (e.g., the user spends too much time in anundesirable posture or does not spend enough time in desirablepostures). Thus, the delivery of posture training and support, as withother digital therapeutics, can be personalized and tailored to thespecific current needs and symptoms of the user 102 a. The posturetraining and support may be provided in response to detected respiratorysymptoms, as a way to alleviate symptoms after onset. In addition, or asan alternative, the support may be provided in advance of symptoms, forexample, to an individual with a high risk of exposure, as apreventative measure to improve habits and lessen the potential impactof COVID-19 on an individual, thus potentially helping to minimize oravoid at least some COVID-19 symptoms.

The system enables posture training and support as a therapeutic, aspart of an approach that provides varying stages of predictive,preventative, and treatment support. The behavioral support forimproving posture can have a number of beneficial physiological effects,such as increased respiratory and diaphragm function, opening the throatand windpipe, reducing pressure on kidneys and other organs, andincreasing circulation. By contrast, bad posture can restrict the throatand airway and can compress the lungs and other organs, which can worsenCOVID-19 respiratory symptoms.

As another example, the computer system 110 and the user device 104 acan provide digital therapeutics to monitor and improve breathing of theuser 102 a. For example, digital therapeutics can include education,instruction, games, and other interactions to train the user 102 a tobreathe more effectively. Monitoring data that describes the user'sbreathing can be collected and analyzed to determine scores for theuser's breathing outcomes. The system can also use active exercises, insome cases monitored or assisted using digital technology, to helpeducate, inform, and assess the user 102 a's breathing performance.

As an example, the system can provide instructions and media to prompt auser to perform, and guide a user through, a diaphragmatic breathingexercise. This exercise can occur when an individual is on their backwith their legs and head elevated. The individual breathes through thenose, expanding their chest and abdomen while positioning their hands onor above the chest and abdomen to monitor the ascent. The user thenexhales through their mouth while monitoring with their hands thedescent. This process is usually timed and continues to repeat,sometimes for a minute or longer. The mobile device 104 a or anotherdevice can optionally be placed on the user's chest or abdomen and canmonitor the ascending movement and descending movement as the userbreathes in and out. The device can collect sensor data on anapproximate change in elevation of the ascension and descensionaccordingly. This data can be used for compliance qualifiers (e.g., toverify that the user successfully completed the exercise as requested)and to measure objective outcomes in improvements (e.g., to determinewhether the user is breathing more easily and more deeply). For example,for an individual that had difficulty at first, the sensor data can beused to determine if the user is later able to breath deeper breaths andhold breath for longer times.

Another example exercise that the system can instruct the user toperform is an incentive spirometer exercise. This exercise can be donewhile an individual is sitting with good posture. After breathing outcompletely, the individual places an incentive spirometer to their mouthand breaths in to their maximum inhalation capacity. The individual thenbreaths out slowly and repeats, sometimes for a minute or longer, whiletaking breaks occasionally. The spirometer is a device that theindividual can breathe into to help strengthen the overall force and airexhalation of the individual's lungs. The device itself can be connectedto a phone or other smart device to monitor volume and exchange of airbetween the participant and the device. Coupled with a phone or otherdigital device, this exercise can further be gamified through digitalcounting and goal setting. These and other breathing exercises can beprovided by the system at regular times as part of a care plan developedfor a patient. As with other digital therapeutics, the intensity ofsupport, e.g., the frequency and duration of the exercises, can bepersonalized and adapted for the user and the symptoms that aredetected, whether through self-reported data or through passive sensing.

As another example, the user device 104 a and/or the computer system 110may cause digital therapeutics to be provided to assess and improve moodor mental health of the user 102 a. Various interventions available inthe system related to an individual's changing mood or behavior can becharacterized as either mental health risk prevention or mental healthrisk detection.

For example, for therapeutic mental health risk prevention, variousinterventions can be provided to produce positive outcomes. One isprompting a user to engage in regular communication with others. Thiscan include reminders to individuals to talk to people and engage inconversation. Communication can stimulate the mind and provides renewedprocessing of information. The level of communication of the user 102 acan be measured through tracking of phone calls, application usage, andsocial media presence on the user device 104 a. The measures can becompared to the user's baseline measures for communication through thosechannels. The system can also measure and provide recommendations orbehavior change interactions for each of the following areas:

-   -   Healthy Diet—the system can initiate interactions to check-in on        meal plans and calorie consumption tracking among micro and        macro nutrients consistent with challenging regular healthy        eating behaviors. This can be cross-compared with mood and sleep        related measurements as contextual baseline and further changes        to the baseline.    -   Sleep—processing of consistent sleep and duration of guidance        based on the age of the individual, utilizing sensors in the        bed, ring sensors (e.g., worn on the user's finger), and other        wearable devices or sensors capable of physiological, behavioral        or environmental related context to sleep duration and sleep        quality.    -   Exercise—regular exercise plans and time of the day as context        to other situations like sleep. The system can perform automatic        detection of exercise in a wearable device such as a watch, ring        or a mobile device.    -   Caffeine Intake—specific consumption as it relates to energy        levels throughout the day and into the night, prohibiting good        sleep through contextual cues.    -   Alcohol—dehydration and sleep cycle disturbances can be        identified as context to sleep phase alterations and arousal        events and other abrupt shifts in brain wave activity.        Disturbances can be measured using EEG data and heart rate        variability HRV.    -   Blue Light Exposure—measuring reduction in screen time from        devices like television, laptops, and mobile devices before        bedtime.    -   Meditation—measuring EEG and HRV changes during meditation        related events prior to bedtime to assist with restfulness.    -   Consistent daily events—keeping on track with sleep time, meals,        exercise and other routine behaviors can support healthy        chemical levels in the mind and support recovery and overall        mental loading.

As therapeutic mental health risk detection, mental health can bemeasured by various surveys and battery tools, gamification tools, andresponses through physiological monitoring. In general, the system canmeasure and record COVID-19 signs and symptoms. The system can beconfigured to ask a user daily questions, such as did you go outside,how are you feeling, is a certain factor better or worse than yesterday?Across a community of users, the system can determine astatistically-relevant set of users that cuts across differentcontexts/situations. For example, the system can identify that exposureto blue light is a trigger for sleep disruption and mental health issuesthat may be particularly significant for individuals already dealingwith other COVID-19 symptoms. Some of the tools for mental healthassessment include the PHQ9 assessment for depression severity, whichcan be useful in monitoring the severity of depression in response to atreatment. Where while providing a new pharmaceutical or othertreatment, an individual's risk of depression could worsen and thus leadto other risks related to their well-being. The computer system 110 mayquantify those risks and inform clinical staff and/or provide supportinginterventions to the individual. The system can store a baseline measurefor the person with respect to depression, as well as data indicatingthe medications user is already taking. This can enable the system todetermine combinations of medications to avoid, either based on priorresearch or by assessing the outcomes for users after being prescribedthe drug.

As another example, the system can provide a Balloon Analogue Risk Task(BART) as a conceptual frame for the individual's balancing of rewardversus loss. Risk can be associated with an individual's willingness toput themselves in a position of risk for a benefit or gain of some kind.Such risk could be further exposure to COVID-19 when treatment appearsto show positive gain. As an example, a person contracting COVID-19 maybe less of a risk taker, at their initial diagnosis when symptoms limitoptions. As the person starts to improve, however, they may increasetheir tolerance for risky behavior, including behaviors that maypotentially infect others. If risk tolerance is determined to beincreasing, the system can provide warnings, alert the user to theirrisky behaviors, and so on. If the computer system 110 detects trends orpatterns of risk changes in certain classifications of individuals orcertain situations (e.g., approaching locations with at riskpopulations), the system can warn the individuals, remind them thattreatment is not complete yet, remind them to wear a mask, etc. Thecomputer system can also define markers for the factors that relate tochanging risk tolerance to enable the computer system 110 to track andpredict it.

As another example, the system can be used to instruct and monitorexercises in order to treat COVID-19 and to monitor disease progress orrecovery. A user can be instructed to wear or hold a device duringcertain exercises. A sensor patch, sensor ring (e.g., worn on a finger),a smart watch, or other device may be used for monitoring. The senseddata can detect changes in physiological parameters, such as heart rateor breathing rate, as the user changes position changes (e.g., fromseated to a standing position) or performs exercise. Among other itemsmeasured, the system can collect data indicating the time of theactivity, duration, physiological measures for quality of breathing,heart function, etc. Responsive to those monitored parameters, thesystem can provide interventions, such as providing warnings,instructing the user to slow down, ending an exercise session, and soon. The system can also set and monitor user goals, and try to push theuser through goals from week to week through a treatment and recoveryplan

Various pulmonary rehabilitation exercises exist for individuals who arerecovering from COVID-19. In addition to the breathing exercises notedearlier, some other exercise therapeutics that the user device 104 a andcomputer system 110 can provide include:

-   -   Sit-to-Stand Squats—Sit in a chair, extend your arms forward as        a counter-balance, and rise to a standing position. Relax, then        place arms out again, and sit down while maintaining posture.        Repeat.    -   Standing Marching—Stand, pull a knee in the air, balance and        then set it down. Repeat.    -   Seated Arm Reaches—Sit in a chair, cross arm in front and then        up and across the body slightly arching the back with finger        pointed up. Repeat.    -   Standing Heel Raises—Stand, come up on tippy toes, then back        down to heals. Repeat.    -   Sidestepping—Stand legs together, take a lateral step to one        side. Repeat.    -   Wall Pushups—Stand arms extended forward with palms flat against        the wall, lower into the wall and then push back away from the        wall. Repeat.    -   Walking—increasing the number of minutes each week during        recovery.

Risk measures are collected before and after and, in some cases,throughout the given exercise. For example, throughout exercise, SpO2should never go below 88%. The user device 104 a, as directed by anapplication or downloaded module 112, can monitor the SpO2 value compareit to the threshold, and initiate interventions if the threshold iscrossed (or if the user is in a range near the threshold). A healthyrange is above 95%, depending on the individual. Reduction in oxygen orSpO2 can result in passing out and damage to one's self, creatingfurther risk of breakage and recovery risks in individuals with higherfrailty concerns. Sustained low oxygen can lead to further organ andsystematic shutdown of the body.

The system can educate and advise individuals about the recoveryprogress and goals, e.g., for the day or for the week, and areencouraged while systematically monitored for risk. The measurementsthat the system makes during the various exercises and interventions canalso be used by the system as a diagnostic tool. For example, during theposes and exercises notes above, decreases in oxygen or SPO2 (or changesother parameters, e.g., respiration rate, blood pressure, heart rate,user report of light-headedness or shortness of breath, etc.) of certainmagnitude may be indicative of or predictive of clinically-relevantparameters. These parameters may include the overall severity ofCOVID-19 effects, the onset of specific additional symptoms or diseaseeffects, the current stage or progression of COVID-19, risk levels forthe type or severity of future disease symptoms, length of time ofrecovery or future impairment in function, and so on. Using dataanalysis or machine learning techniques to assess monitoring data ofdifferent users, the computer system 110 can identify which monitoredphysiological parameter changes or ranges during certain activities arepredictive of which current or future outcomes. Then, the computersystem 110 can detect when the monitored data during an interventionmeets the identified combination of factors that triggers the need foran intervention.

Machine learning can be used to determine which user actions areeffective to treat a disease such as COVID-19, and which actions aremost effective for different users and situations. For example, thecomputer system 110 can perform analysis to determine the types andranges of postures that lead to reduction in symptoms and earlyrecovery, as well as types and ranges of postures that may worsen orhave no effect on symptoms of the disease. This information can be usedto identify what constitutes good posture for a COVID-19 patient, orwhich postures and posture changes are most helpful for specificsymptoms. This analysis process can include determining, based on theexamples of longitudinal data of many individuals, different posturesand posture parameters are most helpful for patients of differentattributes (e.g., different body types, different ages, differentunderlying health conditions), for different disease states (e.g., earlyonset of COVID-19 vs. later, different levels of severity of thedisease, etc.), and so on. This can enable the system to provide apersonalized, contextual treatment recommendation customized for eachindividual, based on factors such as the patient's age, sex,comorbidities or chronic conditions, physiology (e.g., height, weight,etc.), and so on. The results of analysis may be provided as thresholdsor ranges used to select and provide treatment. As another example, therelationships may be implicitly learned through training of a model,such as a neural network, to output data scoring different postures andposture parameters, or different posture interventions, based on aninput data set indicating characteristics and symptoms of a user. In thesame manner, in addition to or instead of analysis and modeling forposture-related parameters, the system can use analysis and machinelearning to determine which factors provide greatest improvement forrespiration training, exercise, mental health, and other treatmentareas.

FIG. 11 shows how the computer system 110 can be used to track andpredict risk factors for COVID-19 for communities. Using community-leveldata as well as individual-level monitoring data, the computer system110 can provide customized disease tracking and disease predictions formany different communities.

As used herein, a community refers a group of people. The group ofpeople in a community can be defined in different ways, e.g., based onone or more of geography, shared characteristics of individuals,membership in a group, interests, etc. For example, a community can bedefined as a group of people associated with a particular geographicalarea, such as a state or province, a county, a city, a zip code, aneighborhood, etc. The community may be the group of people that residein the area, the group of people that work in the area, a group ofpeople that work or reside or otherwise visit the area, etc. Thecomputer system 110 can store data that defines and describes differentcommunities. The stored data can associate a unique community identifierfor each community and data describing the criteria for membership inthe community, e.g., geographical boundaries of the community and otherfeatures of the community. A community may be defined independent of ageographic area, for example, such as a family, a group of people thatwork for the same business, a group of people that work in the sameoffice or building, the members of a certain organization, individualsin a certain profession, a set of people that have visited ageographical area within a certain time (not necessarily permanentlyresiding in that area), etc. In some implementations, a community can bea cohort of individuals enrolled as participants in a research study,which may involve ongoing monitoring or data collection as part of thestudy protocol. In various examples below, a community refers to theindividuals that reside in a particular geographical area. Differentcommunities can refer to different geographic areas and the people whoreside in them, for example, different neighborhoods, different zipcodes, etc.

The predictions and recommendations for a community can be provided todevices of users associated with a community. This can includeresearchers studying the disease, doctors treating patients in thecommunity, government leaders in the community, business owners forbusinesses having locations in the community, and so on. The predictionsmay be provided to individual members of the community.

As discussed above, the computer system 110 can track the behaviors,physiological attributes, and medical records of many differentindividuals. This is represented in FIG. 11 with individual data 1110that has been collected. The computer system 110 can store or accessindividual data 1110 for many different individuals, not only those inthe specific community of interest. For example, while information aboutthe those who reside in a community has a high impact on the community,the health and behavior of outside visitors the community has an impact,as does the health and behavior of those in outside areas wherecommunity members visit.

The system 110 can use machine learning techniques to learn from theoutcomes experienced in many different communities of diverse types.Predictive models 1130 use the information learned through a generationor training process to give better predictions for individualcommunities. This process can be repeated to continue updating andrefining the models 1130 as new data becomes available. In particularthe process of model generation or training can include using manyexamples from different communities to represent how different factors(e.g., community characteristics, individual behaviors, physiologicaldata, etc.) affect different disease outcomes, e.g., for transmissionfor COVID-19, for impact on community disease measures for COVID-19, andso on.

The predictive models 1130 can be machine learning models, for example,one or more neural networks or classifiers. Other types of models thatmay be used include support vector machines, regression models,reinforcement learning models, clustering models, decision trees, randomforest models, genetic algorithms, Bayesian models, and Gaussian mixturemodels. Different types of models can be used together as an ensemble orfor making different types of predictions. Other types of models can beused, even if they are not of the machine learning type. For example,statistical models and rule-based models can be used. The computersystem 110 can analyze the training data about many individuals andcommunities to determine relationships between data factors and COVID-19disease outcomes and other effects on individuals, locations, andcommunities, either through explicit analysis or through machinelearning training, e.g., so that a model implicitly learns thepredictive value of different data items on, for example, diseaseexposure, disease susceptibility, infection likelihood, etc. Trainingcan incrementally or iteratively update the values of parameters in themodels 1130 to learn the impact of different factors on predictedoutputs. In the case of neural networks, backpropagation can be used toalter neural network weights for neurons at various layers of a neuralnetwork model. Training can be done using stochastic gradient descent orother training algorithms.

The computer system 110 acquires and stores community data 1112 for eachof various communities. The community data 1112 can be collected formany different communities on an ongoing basis, to update the predictivemodels and to be able to provide accurate, up-to-date inputs to themodels for making predictions. The community data can includeinformation such as demographic data for the community, mapping data forthe community, traffic data indicating movement patterns and trafficflows, economic data indicating the types of businesses or industriespresent in the community, and more. The community data 1112 alsoindicates disease measures for the community, such as results ofCOVID-19 testing, COVID-19 predictions for the community, transmissionrate metrics, and more. The community data can indicate periodic (e.g.,daily) counts of individuals in the community who have (i) beendiagnosed with COVID-19, (ii) been hospitalized for COVID-19, (iii) diedof COVID-19, or who have been affected in other ways. In addition, thecommunity data 1112 can indicate the policies and rules that a communityhas put in place, including those effective currently, those in effectin the past (e.g., and the times they were used), and those planned orscheduled for the future. These disease prevention measures can include,for example, social distancing recommendations, mandatory businessclosures, restricted occupancy guidelines, mask usage instructions, andso on. As discussed below, this information about the nature of manydifferent communities (e.g., their population attributes, geography, andother characteristics), as well as disease measures over time providesthe computer system 110 with many instances of training data that can beused to train machine learning models to predict disease risks andoutcomes for other communities, as well as the disease preventionmeasures that may be most effective for different communities.

As part of training the models 1130, the computer system can generateand use training data designed for the particular model or type ofprediction. From the set of collected data about different communities,the system 110 can extract may different examples including (i) featuredata indicating the situation in a community at one point in time, and(ii) corresponding outcomes that the model is designed to measure, suchas a score representing a measurement or calculated statistic for thecommunity. During training, the feature data represents an input to themodel, and the corresponding outcome data represents the trainingtargets used to guide the training of the model.

For example, a model can be trained to predict the next day's number ofnew COVID-19 infections for communities. For training this model, timeseries data indicating the actual numbers of new infections day by daycan be used, along with other collected data. The feature data for anindividual training example can represent the state of a community at apoint in time, such as information indicating current and priorcommunity disease measures for the community, community characteristics,user behavior patterns in the community, and so on, all reflecting thestate of a particular community on a particular day. Different examplescan reflect data for different communities and different days. Thefeature data or that state of the community can be labeled with thecorresponding outcome, which in this example is the actual, recordednumber of infections for the subsequent day, e.g., the day following theparticular day that the feature data describes. A neural network modelcan be trained through backpropagation or other techniques, feature dataas an input to the model and using the corresponding outcome as atraining target for the model. As a result, the model can be trained toreceive input describing the state of a community on a particular day(e.g., day N) and output a prediction of the number of new infectionsfor the next day (e.g., day N+1). This technique involves an offset(e.g., one day) between the time the input feature data represents andthe time the training target or model output represents, and differentoffsets can be used to predict conditions at different points in thefuture. Further, no time offset is needed. For example, the input andoutput may both represent the same time period. A model to predict thedisease transmission risk of a location can use current informationabout the location (e.g., location characteristics, location type, etc.)and other current or recent data (e.g., user behavior patterns,community disease measures, disease prevention measures in place, etc.)to predict a score indicating the current disease transmission risk ofthe location.

The computer system 110 can include one or more community impactprediction models 1130 a that predict the impact of COVID-19 on acommunity. This can generate a prediction of current effects of thedisease on the community, which may be very helpful when data gatheringis limited or results are delayed. The impact scores 1121 canadditionally or alternatively indicate predicted impact at a futuretime, such as the next day, the next week, the next month, the nextyear, and so on, or even an overall permanent or cumulative impactexpected.

The community impact predictions can be made for each of multipledifferent dimensions, such as predicted impact to health of members ofthe community, the economy of the community, the health care capacity ofthe community (e.g., hospital utilization and stress on health careproviders), and more. To predict the impact scores 1121, the computersystem 110 can use a prediction model 1130 a that has been trained,based on, the training data of many different communities, to predict ascore based on input data indicating features such as community diseasemeasures, community attributes, and current or recent behavior patternsfor members of the community. Measures of behavior can take variousforms. For example, the computer system 110 can aggregate trackedinformation about individuals and provide statistics about frequency oftravel, types of activities performed, distance of travel, duration ofactivities, and so on. As another example, the computer system 110 mayuse the individual behavior data to classify certain behavior patternsinto classes, and the class assignments can be used as inputs to aprediction model 1130 a. For example, a community's level of use ofpublic transportation may be classified on a numerical scale, orindistinct classes such as high, medium, or low, or statistics aboutpublic transportation usage (e.g., number of daily trips for bus, metrosubway, etc.) can be provided.

Beyond predicting exposure to COVID-19 and risks or impacts expected,the computer system 110 can also identify hotspots of risk or exposurein a community. These hotspots can be areas where increased or hightransmission of COVID-19 has occurred or where it is predicted to occurin the future. For example, the computer system 110 can use records oftravel and activities of individuals within a community to identifyareas associated with COVID-19 exposure. This can be areas whereindividuals known to be infected with COVID-19 have been, locationswhere users having high likelihoods of infection have been (e.g., basedon infection likelihood prediction measures rather than diagnosticresults for earlier detection and better prevention of transmission),and so on. The information about diagnosed patients and those expectedor predicted to have COVID-19 can be used along with behavior dataindicating the typical and recent patterns of traffic, travel, activity,and other behavior of people in a community. These parameters, alongwith other community measures, such as COVID-19 infection rates andother disease measures, can serve as input to a prediction model 1130 b.The hotspot prediction model 1130 b can be one that has been trainedbased on examples of disease transmission in this community andpreferably many other communities.

The computer system 110 can also make predictions about future diseasemeasures for a community. These predictions can be based on behaviorpatterns of individual users that are tracked by the system. Forexample, rather than simply extrapolate from previous infection rates,the computer system 110 can use a community disease prediction model1130 c that takes into account community-specific factors such asdistribution of different types of locations, traffic patterns withinthe community, actual visits of individuals in the community to specificlocations, and so on. As discussed above, the training of the model 1130c can involve training data sets that include (i) feature datarepresenting information about the state of a community at one time(including current and prior disease measures at that time) and (ii) alabel or training target indicating a desired community disease measurecreated at a future time. The times of the different examples may bedifferent, but for across all of the examples, the time offset (e.g.,difference in the time the feature data represents and the time thelabel or training target represents) can be consistent. Through thetraining process, the model learns to predict future disease measures,specifically, measures expected at the a time having the time offset. Ifthe time offset used in training is a week, then the model will betrained to predict the disease measures that will be present a weekafter the time represented by input to the model 1130 c. Examples ofcommunity disease measures that can be predicted include valuesquantifying cases of a disease, hospitalizations for the disease, deathsfrom the disease, rates or percentages of positive and negative testsfor the disease, RO metric (e.g., measure of how many people an infectedperson infects), etc.

The computer system 110 can also recommend disease prevention measuresfor a community. For example, given the characteristics of the communityand the tracked behavior patterns of members of the community, thecomputer system 110 can generate a set of input data for a preventionmeasure prediction model 1130 d. The prediction model 1130 d can be onethat has been trained based on data indicating communitycharacteristics, community disease measures, and the preventionmeasures, if any, that the respective communities have enacted. Thetraining data can include time series data or other indications ofdisease prevalence over time, as well as the disease prevention measuresused and compliance rates over time.

Through the process of training, the prediction model 1130 d learnswhich disease prevention measures are correlated to changes in diseasetransmission and other disease measures, as well as the timing of thosechanges. For example, instructing individuals to wear face masks mayreduce disease transmission, but may do so at different rates ordifferent magnitudes of effect for different types of communities (e.g.,communities with different combinations of characteristics, or fourcommunities at different stages of an outbreak (e.g., for certaintransmission rates, or certain ranges of exposure to the population as awhole, and so on. as preparation for training the prevention measureprediction model 1130 d. The computer system 110 can process theavailable training data and extracted specific examples of instanceswhere prevention measures were initiated, changed, or removed and thecorresponding changes in disease measures over one or more times (e.g.,the next day, each of the next seven days, the next month, and so on.The model 1130 d can be trained using a cost function or objectivefunction that rewards certain types of changes in disease measures(e.g., decreases in transmission rate, decreases in new infection rate,and so on) and penalizes others (e.g., increases in transmission rate,increases in new infection rate, and so on). In addition, or as analternative, a label or training target can reflect an assessment of howwell a given disease prevention measure performed. For example, based onexamining collected data and patterns occurring over time, aneffectiveness score of 7 can be assigned to reflect how well masksbenefited a community at one time. For a data set representing adifferent community or different situation an effectiveness score of 5may be assigned, showing that less benefit was achieved. The model 1130d can learn, through training with this type of effectiveness scoretargets, to predict effectiveness scores along the scale used to createthe labels, thus learning how to assign effectiveness scores based oninput data when the actual results are not yet known.

The output of the model 1130 d can be a value for each of a set ofrecommend prediction measures prevention measures, (e.g., a score offive for maintaining or implementing mandatory face mask usage, a scoreof three for closing certain types of locations in the community, and soon). The set of outputs from the model 1130 d can indicate the relativepredicted effect or absolute effect of enacting the different preventionmeasures the model 1130 d is designed to assess.

The prediction scores for the community can be provided as input to theindividual prediction models 230 discussed above. For example, thecommunity impact prediction scores 1121, infection hotspots 1122, anddisease prediction scores 1123 can affect the exposure score andinfection likelihood score for an individual. For example, high orincreasing disease prediction scores 1123 for an individual's communitycan increase the user's exposure score and likelihood of infectionscore. As another example, if a user is determined to have visited anarea identified as a hotspot of exposure, the information may be used toincrease the exposure score for the user. Similarly, a community impactscore indicating that hospitals are near capacity may prompt the systemto choose more aggressive testing and treatment approaches, for example,to send testing kits earlier or for lower exposure scores to give thecommunity earlier or more comprehensive view of an outbreak.

The individual prediction scores that are based on the communitypredictions can then be used to personalize testing and treatment. Forexample, based on the community-level predictions and their effects onindividual predictions, the system can initiate disease managementactions including sending disease testing kits, selecting orrecommending a vaccine, recommending or initiating digital therapeutics,recommending or providing pharmaceuticals or other treatments, and soon.

FIG. 12 shows an example user interface 1200 showing data determinedbased on the predictions 1120 for a community. The user interface 1200can be provided to business or government leaders for the community, toresearchers, to members of the community, or others.

The user interface 1200 shows infection rate data 1210 which includedata indicating tracked measures of actual infection rates (e.g., stableover the last three days), as well as a prediction of future infectionrates (e.g., increase predicted over the next 2-3 days). Thisinformation is also reflected in a chart 1240 or other visualizationwhich can show the pattern of change infection rate that is predicted.

The user interface 1200 shows an indication 1220 of factors contributingto the expected infection rate changes. These include the fact thatincreased travel has been detected in the community and that theinfection rate has increased in neighboring communities that members ofthis community visit. These factors can be determined through analysisof the training data 1131 of FIG. 11, which shows examples of differentcommunities, including their members' behavior and characteristics andresulting disease spreading outcomes. Factors that are correlated toincreased disease spread (e.g., increased frequency of travel andinfection rate increases in traveled-to locations in this example) canbe indicated to users. The user interface 1200 indicates a chart 1250 orother visualization that shows actual and predicted levels of travel forthe community, illustrating the factors leading to a prediction ofincreased risk.

The user interface 1200 includes recommendations 1230 that showrecommendations to reduce the spread of COVID-19. These recommendationsare customized for the specific community, based on the community'scharacteristics, the recent disease measures for the community, thegeography of the community (e.g., the number, types, and proximities ofdifferent businesses and other destinations), tracked behavior ofmembers in the community, and so on. For example, the recommendations1230 can indicate prevention measures indicated by the scores 1124 ofFIG. 11. In this example, the recommendations include encouraging areduction in travel to two nearby counties with high infection levelsand also to limit occupancy at a popular restaurant identified as adisease exposure hotspot. Other types of recommendations, includingcommunity-wide recommendations can be determined and provided,customized for the predicted effectiveness of the disease preventionmeasures given the characteristics of the community, the currentcommunity disease measures, the types of locations in the community, thecurrent and historic user behavior patterns in the community, and so on.For example, the recommendations may indicate a level of distancingbetween individuals, whether masks should be required and for whatlocations or activities, whether a general stay-at-home instructionshould be given, etc.

FIG. 13A is an illustration of the feedback that can occur betweencommunity-level data and predictions and individual-level data andpredictions. Each can influence the other. Monitoring results fromindividuals can be used to make predictions about the community. Forexample, aggregated information about individuals can show the patternsand trends for the community as a whole. Similarly, the exposure risksand predictions about the community, in turn, can influence the level ofdisease testing and digital monitoring needed for individuals in thecommunity. Community data provides a way to interpret the data factorsfor individual behavior. The level of travel and social activity of auser can be weighted by the community disease measures, for example.When disease prevalence and infection rates are high or increasing, thesystem can respond with more aggressive testing and monitoring.Similarly, the system can provide digital therapeutics and even vaccinesand medications, based on collected data meeting corresponding criteriaor thresholds, to address infections early. For example, certainmedications or vitamin supplements can be recommended or even prescribedor shipped to a user based on community exposure levels, as can medicaldevices, software applications, and more. Thus, all of thecommunity-level factors ultimately affect the diagnosis and treatmentfor individuals.

In further detail, community-level measures can influence individualrisk levels. As discussed above, the predictions of level of exposurethat an individual has experienced can be determined based on measuresof disease prevalence and transmission in the user's community. Forexample, the current infection rate measures and/or predicted infectionrate measures can influence the prediction of how likely a user has beenexposed or is likely to be exposed. This in turn leads to differentmeasures for the user's infection likelihood.

For example, two users with similar behavior patterns may have differentexposure levels or risk levels based on differences in theircommunities. For example, a first user may be in a community whereinfection rates are determined to be increasing or are predicted toincrease. On the other hand, a second user may be in a community whereinfection rates are stable or decreasing, or are predicted to decrease.As a result, because the communities of the two users present differentlevels of exposure risk and likelihood of infection even with similarbehaviors of the users.

The data collection and predictions for a community and for individualscan interact in different ways. Observations about an individual can beused to select and provide disease management (e.g., treatment,monitoring, and exposure prevention) for the individual, as well asother individuals that the user has come into contact with. In addition,data for a community can lead to disease management for the community asa whole. For example, when community disease measures reach certainreference levels, the system 110 can provide testing kits, surveys,digital therapeutics, mask and distancing policies, and other diseasemanagement elements can be provided throughout the community. Data for acommunity can also be used to select and provide disease management forspecific individuals in the community. For example, the system 110 mayrespond to increasing community disease measures by recommending orproviding vaccines or heightened monitoring for users whose measures ofsusceptibility or disease exposure satisfy certain threshold. Data foran individual can also be used to select and provide disease managementfor the community as a whole. For example, the positive test of oneindividual in a community may prompt monitoring, testing, and treatmentfor some or all others in the community, even if the others were notdetermined to or likely to have had contact with the individual.

The collected data for one or more individuals can prompt diseasemanagement actions for the broader community that the individual is amember of. For example, if a user is diagnosed with COVID-19 (e.g.,tests positive or has significant signs or symptoms of the disease),then monitoring and/or treatment for the disease may be initiatedbroadly throughout the community. In some implementations, the computersystem 110 determines and implements treatment for a disease, such asCOVID-19, across a community. Consider a case where a students in aclass are going on a trip and will be in proximity on a bus, plane, orother setting. The susceptibility of one or more of the students to adisease can lead to a recommendation for vaccination of all of thestudents in the class. Similarly, if one student show symptoms of thedisease or tests positive (e.g., after a trip together or after regularclassroom interaction), then treatment may be applied broadly to theentire group, since all were likely exposed together (e.g., due to closeproximity, visiting the same locations at the same times, etc.). Thus,in some situations, if one member of a community shows signs andsymptoms of disease, treatment can be provided for the entire community,because it is probable that all are infected. A community in thissituation can be a class of students, a sports team, a business, afamily, or other group of people that is in proximity to each other.

The computer system 110 may select and provide any of a number oftreatments for COVID-19 across a community as a whole when anappropriate condition is detected (e.g., one or more community memberstest positive, have signs or symptoms, have an infection likelihoodabove a threshold, etc.). Digital therapeutics are a good option, suchas interventions to encourage physical exercise, breathing training,posture training, and so on. These interventions can be a low-risk formof treatment that are likely to benefit and not harm users that do nothave the disease. These interventions can strengthen a person'sdiaphragm and chest muscles and help establish habits of deeperbreathing (e.g., increase breath volume), which can limit at least somerespiratory symptoms of COVID-19. These therapeutics can be especiallyhelpful when in the face masks are used, which when worn may cause someindividuals to breathe more shallowly and take fewer deep breaths. As aresult, people may not be exercising their muscles to the degree neededto prepare for or overcome COVID-19 infections. Specific digitaltherapeutics can be evaluated and qualified for treating COVID-19 andits symptoms, and in some cases may receive or require approval fromregulatory agencies such as the U.S. Food and Drug Association (FDA).

In some implementations, the computer system 110 may select andrecommend a preventive measure, such as a vaccine, across an entirecommunity or a portion of a community, based on planned events orexternal aspects. For example, even if community disease measures arelow in a first community, COVID-19 cases in nearby communities may leadthe computer system 110 to recommend vaccinations in the firstcommunity. Similarly, if one or more people in a community (e.g., schoolclass, business, sports team, etc.) are planning travel to or activitiesin an area with high exposure risk, a vaccination, medical device, ortreatment may be provided to all of the members of the community. Thuspredictive prevention steps and monitoring can be based on plannedactions and the exposure that might occur.

FIG. 13B shows an example in which the community exposure predictionscore for a community has increased and the system provides an alert toa user 1310 through a device 1315. In this example, the level ofexposure in the community has increased, and the system instructs theuser 1310, with a notification 1320, to start a digital therapeuticsprogram for breathing monitoring and training. This program can have amultiple goals, such as to detect breathing difficulties at an earlierstage, to strengthen the user's breathing, and to start good habits inthe event that the user does contract COVID-19. The computer system 110and the device 1315, including an application on the device 1315, cancooperate to initiate interventions using the digital therapeuticsprogram, perform monitoring, and take other actions based on thecommunity exposure level.

FIG. 14 shows an example of a system that can use geofencing or otherlocation tracking techniques to monitor and characterize COVID-19exposure risk. The system can use passive sensing by a mobile phoneapplication to determine COVID-19 exposure risks. The system can alertan individual when behaviors result in significant risks, e.g., when anaggregate level of exposure is reached or when a specific action orcondition associate with exposure risk occurs.

In the example, a user 1410 has a user device 1420, such as smartphoneor other mobile device. A software application is installed on thedevice 1420 to enable various interactions with the user 1410, includingproviding prompts to the user 1410, receiving responses (e.g., inputsand interactions) from the user 1410, providing recommendations,collecting sensor data about the user 1410, and so on. The applicationsends user entered data 1411 to the computer system 110 over acommunication network 1430, which can be a public or private network andcan include the Internet. The application also sends sensed data 1422 tothe computer system 110. The sensed data can include sensor data ormeasurement results generated using one or more sensors of the device1420 or of another device. For example, the mobile device 1420 maycommunicate another device over a wireless connection (e.g., Bluetooth,Wi-Fi, near field communication (NFC)) to request and receive senseddata 1422, and in some cases can even initiate measurements with theother device. Examples of other devices that can communicate with thedevice 1420 and provide sensor data or measurement results include asmartwatch or other wearable device, a medical monitoring or treatmentdevice (e.g., a pulse oximeter, a digital thermometer, a glucometer, aweight scale, etc.).

Data can be collected from the mobile device 1420 in a repeated orongoing manner, e.g., periodically at an interval, on-demand asrequested by the computer system 110, in response to a condition beingmet, etc. In some implementations, data is streamed in substantiallyreal time, such as in response to detecting changes in conditions of acertain type or magnitude, thus providing the computer system 110 a datafeed indicating the user's current context, location, activity, andother information so the computer system 110 can detect and respond tothe current situation the user 1410 is in. Further, the reported datacan be recorded and processed by the computer system 110 to determinethe pattern of behavior over time, such as the progression from locationto location at different times and the corresponding environmentalconditions, user inputs, context data, etc. for those times. While onlyone user 1410 and corresponding device 1420 are illustrated in FIG. 14,the computer system 110 can receive data from and interact with devicesof many different users, including different users in a single communityand users in many different communities.

The behavior of the application can also be adjusted throughcommunication from the computer system 110. The computer system 110 cansend configuration data, content, rules, or other data that changes aconfiguration of the application. The computer system 110 can alsoprovide messages that instruct or otherwise cause the application toinitiate interactions with a user. For example, the computer system 110can send transmissions 1440 that cause the mobile device 1420 to presentnotices or notifications, alerts, recommendations, digital therapeuticsinteractions, surveys, ecological momentary assessments (EMAs),ecological momentary interventions (EMIs), games, and so on. Thecomputer system 110 can instruct the application on the device 1420 topresent specific content (or initiate specific interactions) at specifictimes or in response to detection of specific circumstances. Examples ofinteractions that may be provided for users are discussed further below,including interactions to recommend disease prevention actions, to alertthe user 1410 to COVID-19 cases or exposure events nearby, to warn theuser 1410 of risk levels or risky events (e.g., such as entering an areawhere COVID-19 exposure has occurred), and so on.

The computer system 110 determines which interactions to initiate andthe timing of interactions by evaluating the collected data for the user1410, including user-entered data 1421, sensed data 1422, health recordsfor the user 1410 (e.g., EHR/EMR), user scores 240 (e.g., predictionsfor disease exposure level, disease susceptibility, likelihood ofinfection, and so on). In addition, the computer system 110 can usecollected data for other users in the same community as the user 1410,as well as data for the community of the user 1410 generally (e.g.,disease measures for the community, demographics for the community, mapdata for the community, etc.) to select and initiate interactions.

The computer system 110 collects, stores, and uses various types of data1450. The data 1450 includes location tag data 1451, passive sensingdata 1452, population data 1453, location data 1454, and community data1455. Other data can be stored including user interaction data (e.g.,responses to prompts, data entered into forms, application and deviceusage statistics, etc.), health record data, and so on. The location tagdata 1451 indicates location-based records of events and conditions thataffect disease exposure. A location tag refers to a record, entry, ordata element that corresponds to a specific location, and so a locationtag is a record that “tags” or provides information (e.g., an event,condition, status, etc.) for a certain location or region. A locationtag can be applicable only for a certain period of time, e.g., a windowof time or can apply to a discrete event. There are various types oflocation tags, and these will be discussed further below.

The passive sensing data 1452 represents data collected through passivesensing techniques, such as without requiring user action to initiatethe data capture and often without any output or indication to the usersignaling that measurement or data capture has occurred. An example isthe automatic capture of sensor data from sensors of a device, e.g., ameasurement or output from a GPS sensor, a compass, accelerometer, ainertial measurement unit (IMU), a light sensor, a heart rate sensor, acamera, and so on. The sensors used for data capture can be part of thedevice that runs the application for data collection and userinteraction, e.g., a user's smartphone, or on another device, such as asmartwatch or other wearable device, a medical device, a dedicatedsensor, etc. Passive sensing be performed by the application initiatingsensor data capture automatically, for example, on an ongoing basis(e.g., periodically at a determined interval) or in response to theapplication or the computer system 110 detecting a condition (e.g., inresponse to a change in behavior, location, etc.). Many different typesof data can be captured using passive sensing, including location data,environmental data, physiological data, behavior data (e.g., regardingsleep, exercise, physical activity, movement, movement profiles fordifferent tasks or activities, etc.),

The passive sensing data 1452 includes the recorded data streams fromthe various users. The sensor data for each user can be associated withmetadata indicating the context in which the data capture occurred(e.g., a timestamp, a location, etc.), as well as a user identifier forthe associated user and a community identifier for the user's community.

The population data 1453 can indicate the population level at differentlocations. This can include measures of population, population density,occupancy, traffic, etc. Population can be determined at a fine-grainedlevel, e.g., for specific regions or portions of a community, such asfor different neighborhoods, city blocks, subdivisions, buildings oreven for specific addresses.

The location data 1454 can indicate characteristics of differentlocations in one or more communities. For example, the location data1454 can include map data indicating where different locations are inrelation to each other. The location data 1454 can also indicatelocation types and other location characteristics, e.g., whether alocation is indoors or outdoors, building characteristics (e.g., squarefootage, year built, type of ventilation system, etc.), occupancy levels(e.g., current, average, maximum, etc.), and so on. A community mayinclude may include locations 1458 of many different location types 1459or categories. Not only do the locations of different types havediffering physical characteristics, the locations of different types areoften used for different purposes, and so the patterns of activities andbehaviors of individuals at different types of locations vary (and evenamong locations of the same type), which results in differing diseasetransmission and exposure risks. The location type (e.g., a category orclassification for the characteristics and/or use of the location) ofvisited locations is one of several factors that the computer system 110can use to determine the disease exposure risk that individuals facegiven to their individual activities and travel.

The community data 1455 can include information about various differentcommunities. For example, the community data 1455 can indicate, for eachcommunity, a corresponding community identifier, a geographic boundaryfor the community, demographic data for the community, a classificationfor the community (e.g., urban, suburban, rural, etc.), and so on. Thecommunity data 1455 can also include community disease measures obtainedfrom public health agencies (e.g., CDC, HHS, local governments, etc.) oras aggregations of data collected by the computer system 110 forindividuals. Community disease measures can include values quantifyingcases of a disease, hospitalizations for the disease, deaths from thedisease, rates or percentages of positive and negative tests for thedisease, etc. Measures for confirmed and/or probable cases can becollected, and data for different time periods can be stored to show theprogression of different measures over time.

With respect to the location tag data 1451, there are different types oflocation tags for different factors that affect disease exposure risk.For example, one type of tags may indicate population levels (e.g.,population density, traffic, occupancy, etc.). Another type of locationtags may indicate location types and corresponding different levels ofdisease exposure risk due to the location types. Another type oflocation tags may indicate exposure risk due to visits to locations byspecific individuals, such as when an infected person may leaveinfectious particles behind. All of these types, population-basedlocation tags, location-type-based location tags, and visit-basedlocation tags can be used together to quantify past disease exposure,detect and warn of current disease exposure risks, and initiateinteractions to limit or avoid future exposure.

Location tags of any or all types can also be associated with metadatathat further describes the level or type of risk represented by thelocation tag. One example is a geofence or geographic boundaryindicating the area of influence represented by the tag. Anotherexample, is a disease transmission score that indicates the degree towhich entry into the corresponding geofence or area for the location tagincreases exposure potential. Similarly, information indicating avaccination rate for the location (or for the broader community that thelocation falls within) can be associated with a location tag and may beused to adjust the disease transmission risk. For example, exposurerisks based on population and location type decrease as the level ofvaccination of people at a location increases, and the calculations ofthe computer system 110 can take this into account. For example, thedisease transmission score for a population-based location tag orlocation-type-based location tag can be weighted or scaled according tothe level of vaccination among people at the location (e.g., either atspecific times or generally over a time period). As another example,statistical analysis or machine learning can be used to learn therelative change in transmission risk for different vaccination levels(e.g., the impact of 50% vaccination vs. 60% vs. 70% etc.), to provideevidence-based generation of disease transmission scores reflectingdisease transmission risk.

Another example of information that can be associated with the locationtags is data describing disease prevention measures that are applicablefor the location (e.g., whether masks are mandatory, whether occupancyis restricted, whether distancing of individuals is required, and soon). These measures, and the compliance or adherence of individuals withthem, affect the risk of disease transmission significantly. Communitydata can indicate the disease prevention measures that apply to alocation. In addition or as an alternative, the computer system 110 maycause user devices to present questions that ask users about diseaseprevention measures that are in effect. The computer system 110 maycause user devices to present questions that ask about their own diseasemanagement actions (e.g., “Are you wearing a mask now?”) and those ofothers (e.g., “What percentage of people at this location are wearingmasks?,” “Were there disease prevention requirements for thisbusiness?”). The computer system 110 can be configured to ask about anyof various preventive measures

These types of question can be particularly important to find out aboutthe actions of the person whose visit causes the tag to be created, forassigning disease transmission scores for visit-based location tags. Auser can be asked about his or her mask usage at a location during thevisit (e.g., through an EMA while at the location) or later (e.g., a dayafter the visit, in response to determining from a positive disease testresult that the user was infected at the time). In some implementations,a machine learning model is trained to provide output of diseasetransmission scores for a location, based on input data such as one ormore of community disease measures, location characteristics, populationlevels, behavior patterns for the location, disease prevention measuresin place, compliance levels with disease prevention measures, etc.

The computer system 110 and user devices can use the location tags toinform users of disease exposure risks. For example, the computer system110 or the user device 1420 can obtain location monitoring dataindicating a location or direction of travel of the user device 1420.When the user is detected to be near a tagged location (e.g.,approaching the tagged location, within a predetermined distance of thetagged location, at the tagged location, etc.), the device 1420 providesa notification to alert the user to the current or upcoming risk. Thenotification can inform the user of the risk presented, e.g.,“Approaching a populated area which increases COVID-19 risks,” “warning,a person with COVID-19 was here less than 12 hours ago,” “movie theaterspresent a high risk of COVID-19 transmission,” and so on. Thenotification can additionally or alternatively recommend measures toreduce the risk of disease transmission, e.g., “wear a face mask toreduce COVID-19 exposure,” “move your visit to after 4 pm when the storeis not as busy,” “given diagnosed cases of COVID-19 here, it isrecommended that you return home,” or “Store X has confirmed COVID-19exposure, try Store Y instead.”

In addition to warning users of current and/or prospective diseaseexposure risks, the computer system 110 can use the location tags toestimate the overall exposure that has occurred for an individual. Thecomputer system 110 can track the locations of users and determine whichtagged areas each user enters during a time period (e.g., during a day).The levels of exposure or risk for each event are then aggregated todetermine an overall exposure level for the user. Specific instances ofhigh exposure risk can also be identified and indicated to individualsas well as potentially to community health agencies and governments. Forexample, these high-risk events may be determinations that the visit ofa user to a location overlapped in time with, or occurred within athreshold period of time after, a person diagnosed with COVID-19 visitedthe same location, whether the diagnosis occurred before or after thevisit. Other factors can be taken into account, such as whether thepaths of movement of the users within the location crossed (e.g., if thetwo people passed through the same portion of a store), whether diseaseprevention measures such as masks were used, the duration of the visits,the activities performed (e.g., shopping, exercising at a gym, etc.),the characteristics of the location (e.g., building size, layout,occupancy at the time, etc.), etc.

The analysis of location tracking data and visit-based location tag datacan generate result data similar to that of contact tracing, e.g., byproviding indications of instances where specific individuals overlap intime and location with other users, but does not requiredevice-to-device communication for users' devices and does not requiretrained workers to investigate interactions. The present system can alsocapture high-risk events where there is no overlap in time of visits(e.g., a second user may arrive 15 minutes after an infected first userhas left, but the second user may nevertheless be exposed based onresidual infectious particles). In addition, the present system canquantify the level of exposure or exposure risk that occurred, based ondata that describes the durations of the visits by two users, behaviorsand activities during the visits, the period of overlap or amount oftime between the visits, characteristics of the location, diseaseprevention measures in place at the location and compliance with thosemeasures, and so on.

As location information is determined about users, the computer system110 generates indicates location tags representing visits of users tolocations. When a user's device is located in an area for at least aminimum amount of time (e.g., 5 minutes), a visit-based location tag canbe automatically generated to represent the visit. Each location tag canhave a corresponding record that associates a unique location tagidentifier with descriptive data about the visit, such as the locationof the visit (e.g., an address, GPS coordinates, etc.), a classificationor category for the location (e.g., a business type), beginning time andend times of the visit (e.g., arrival and departure times) and/or theduration of the visit, a path traveled or area covered during the visit,an activity or movement pattern that occurred during the visit, a taskperformed during the visit, conditions present during the visit (e.g.,current occupancy or traffic level during the visit, an indication whichdisease prevention measures were in effect, a level of compliance withthose measures, etc., which may be determined through surveys presentedto the user or other users at the location), and so on. Otherinformation can be associated with or linked to the location tag, suchas a vaccination rate of people at the location, characteristics of or areference to a profile of the user whose visit caused the location tagto be generated, etc.

The location tags can be provided for visits by any individual who hasthe application on an appropriate device, regardless of whether theindividual is a member of the community. It can nevertheless be helpfulto associate location tags with an identifier for the community of theindividual whose visit caused the location tag to be created. Thecomputer system 110 can use this to track the manner in whichcommunities interact and the level of impact that members of differentcommunities have on the disease exposure risk of other communities. Forexample, the computer system 110 can use the visit-based tags andassociated community identifiers to identify other communities that ledto disease exposure for an individual or community, as well as thebreakdown or percentage of disease exposure that resulted from peoplefrom different communities.

As discussed further below, in some implementations, a geofence can beassociated with each location tag. The geofence can represent thegeographic area in which the visiting user may have spread infectiousparticles if the visiting user was infected. The geofence may representa standard area for the location, such as the entire store for a visitedstore, or a more customized area, such as only a particular region ofthe store that the visiting user passed through, as determined by GPSdata, wireless beacon detection data, etc.

Each visit-based location tag can be associated with a diseasetransmission score also. The disease transmission score can indicate theextent or magnitude of disease transmission potential that the specificvisit by a specific user provides for other users that may concurrentlyor subsequently enter the geofence area for the location tag. Thedisease transmission score can be based on various factors, such as ahealth status of the visiting user, e.g., a COVID-19 diagnostic testresult, a diagnosis from a doctor, an infection likelihood scoregenerated by the computer system 110, signs or symptoms of COVID-19detected by the computer system 110, etc. As the health status isupdated for the visiting user whose visit the location tag represents,the transmission score can be updated also. For example, if a user hasnot been diagnosed with COVID-19 at the time of a visit, but thenreceives a positive test result the next day, the disease transmissionscores for location tags for all visits by that user for a prior timeperiod (e.g., the previous 3 days or 5 days) are updated to reflect thelikelihood that the user was most likely infected at the time thosevisits occurred. The disease transmission score can also be based onother factors that affect the potential that the visit had for diseasetransmission, such as disease prevention measures of the visiting user(e.g., whether a face mask was worn), the duration of the visit, thepath traveled during the visit, the activities performed during thevisit, the characteristics of the location, and so on.

Location sensing can be performed using sensors for proximity detectionto other areas, locations, buildings, and devices. Global positioningsystem (GPS) receivers in phones and other mobile devices can be used todetermine device locations with precision. As another example, locationscan be determined based on mobile phones or devices detectingradio-frequency emitting energy sources such as Bluetooth advertisingagents (e.g., location beacons or other mobile devices), Wi-Fi accesspoints, cellular base stations (e.g., cell towers), etc. The strength ofsignals received and the identity of the transmitters can be used tocharacterize a location, e.g., to triangulate a position based onrelative signal strength between different transmitters. Nearby mobiledevices can show relative activity of nearby individuals and populationdensities present at different places at different times.

The system can receive location tracking data from the devices of manydifferent individuals. Location tracking data, such as GPS data anddetected signals from other transmitters provide a stationary fixedpoint at which to associate users and events. Exposure risks due tovisits can be mapped to or associated to these locations using locationtags. When a user is determined to have a COVID-19 infection or a highlikelihood of infection, the locations visited by that user can be givean location tag to indicate the presence or high likelihood of COVID-19exposure at that location. Each location tag can also be assigned ageofenced indicating a geographic region estimated to be affected by theexposure event. The geofence can define an area, e.g., a geofenced area,that can be set based on the path of movement of the user visiting thearea, for example, whether the user was tracked at only a portion of astore or moving throughout the store. Each location tag can be assigneda timestamp indicating the time the exposure event occurred. Thelocation tag can be considered to be a risk to visitors to the geofencearea for some amount of time after the initial exposure event (e.g.,visit by a user). The system can diminish the effect or risk representedby the exposure event for different times, for example, by removing thelocation tag after a predetermined amount of time has elapsed.

Each location tag can be assigned a transmission score, indicating anintensity or severity of exposure estimated to have occurred. Thistransmission score can be based on a number of factors, such as thelocation type, the duration of the visit, the activity of the user thatvisited, environmental conditions, the likelihood of the user having anactive COVID-19 infection, etc. The scores can be assigned using machinelearning techniques in some implementations, with one or more modelsthat have been trained based on examples of different exposure eventsand the corresponding disease transmission outcomes. In addition, thetransmission score may be diminished or discounted based on the amountof time that has passed from the initial exposure event and a subsequentvisit by a different user to the corresponding geofence area. Forexample, the transmission score may be gradually or incrementallydecreased or weighted lower for increasing amounts of time since theexposure event. Thus, a first transmission score may be used to assessthe risk of a user that enters the geofence area an hour after theexposure event, a lower transmission score may be used to assess therisk of a user that enters the geofence area two hours after theexposure event, and so on. The transmission score can be weighted oradjusted according to a pattern of equation for aging of location tags,and this pattern can also be determined through machine learning.

As noted above, the computer system 110 can vary the effective diseasetransmission score or level of influence of a visit-based location tagon the exposure calculation of another user based on the amount of timethat has elapsed since the visit that caused the location tag to becreated. One way to do this is to assign a base disease transmissionscore based on the characteristics of the visit (e.g., exposure event)that the location tag represents. Then, when calculating the exposurescore for a user based on entering the tagged area, a weight or scalingfactor is applied to the tag's disease transmission score to discount orreduce the effect to show lessened risk based on the passage of time.The weight or scaling factor can be based on, for example, the amount oftime between the potentially infected user's departure and thesubsequent arrival at the location of the user whose exposure score isbeing calculated.

The aging of tags, e.g., weighting or scaling the scores based on thepassage of time, can be dependent on the environment at the visitedlocation. As COVID-19 may be is airborne, additional interventions toquantify and reduce the risk of airborne transmission are delivered withthis system. Accordingly, environments with air conditioning, airfiltering, or an open-air environment with increased airflow may presentlower risk and the disease transmission scores for these locations canbe lower than for similar visits to areas with poorer ventilation.Treated air may reduce the aging or time that an exposure risk ispresent to individuals. A message could be delivered as “This area wasrecently tagged, perhaps tomorrow would be better for a visit?” or“Remember to wear your mask, and if possible, delay your arrival by 1 to2 hours.”

The system can support social observation of an individual or acommunity by determining the intersection of geolocation data, dataindicating proximity to others, and/or usage behaviors of mobiledevices.

In some implementations, the computer system 110 classifies geolocationinformation to determine and store information about the type oflocation a user visits, in addition to or instead of the exact location.For instance, knowing that the user is at a pub or a church can be animportant cue that could describe social relevance but also an exposurerisk potential based on the location type. Likewise, a library or auser's home might show low social interaction and thus low risk. Beyondlocation type, the computer system 110 may use indications of actualbehavior, such as responses to a question such as “how many people didyou talk to at the store today?” and sensor data or device-to-devicecommunication data indicating how many other user devices a user was inclose proximity with and for how long.

In some implementations, radio-frequency (RF) sampling can help withpositional guidance inside of a home when considering Wi-Fi, Bluetooth,and cellular signals. This can give a relative guidance to socialexposure indications like being in a public area when GPS isn't shared,and the increase Wi-Fi hotspots or nearby mobile phones and wearablesincrease or decrease in availability. Signal strength, sometimes calleda received signal strength indicator (RSSI), is a key metric collectedthat describes the relative strength between one device and anotherdevice. This can give approximate location information when consideringnearby sources, type of radio, and other contexts known about thestructure and the population densities ratings for the given location.Building structures, human bodies, and other articles can limit signalstrength based on the position of one radio to another. In a free oropen air environment, these signals can produce more predictableresults. This can further describe a level of accuracy when consideringlocation tag creation while entering or exiting a location and thecontext of that location. In other words. when an individual's locationdata is used to tag a location for COVID-19 exposure, that location'squality of data and characteristics describing the location can affectthe score for the location tag.

In some implementations, behavioral data from individuals, such as phoneactivity, can further help in determining if the user is nearby otherphones, which can be used as a measure of risk of exposure. For example,phones may transmit wireless messages, such as Bluetooth advertisementmessages or beacon messages, which can be received by other phones andused to detect which other devices are nearby and the times the deviceswere near each other. The computer system 110 can measure changes inphone usage behaviors of users as part of comparative analysis to otherdigital markers. For example, increased application or screen time usagecan be an indicator of decreased exposure risk in some instances.

Various classifiers can be enabled for day-to-day tracking of behaviorof individuals, as well as to track behavior patterns and trends for thecommunity more generally. When an individual reports COVID-19 symptomsor a community describes outbreaks, information from the individual orcommunity can be used to create location tags within the serverinfrastructure to inform other individuals and the community ofincreased risks in locations or establishments.

To promote and protect privacy, the system can support restrictions onsharing certain privacy-protected “safe harbor” data elements, such asidentifiers for individuals or their devices, that are collected duringthe geofencing and contact tracing process. Once data classifiers aregenerated, information related to “safe harbor” element are marked forexclusion when transmitting data between individuals to the system foruse by the community. Examples of “safe harbor” data elements includeindividuals' names. Names can be replaced by unique identifier for thepurpose of identifying areas of repeated visits of an individual, aswell as determining the frequency and timing of visits in associationwith other individuals who are also identified by their own uniqueidentifiers. The unique identifiers are assigned to the respectiveindividuals but do not reveal the individuals' true names to others inthe system. As another example, phone numbers and IP addresses ofindividuals can similarly be protected. Additional uniquely generatedidentifiers are generated for tracking repeated access, duration ofaccess, calls made, and other elements as behavioral usage informationthat deliver context to the individual's unique personal identifier.

Location tags, along with population data, location data, and passivesensing data, are periodically updated for individuals connected to thesystem and for the community of individuals as a whole. These ongoingdata feeds allow the computer system 110, potentially in coordinationwith another server, to deliver notices, alerts, and phone calls toindividuals as methods of intervention, both to a specific individualthat may be facing a disease exposure risk and to other individuals inthe community.

For example, using the data 1450 shown in FIG. 14, the computer system110 can determine risk factor indices for communities and individuals(step 1460). For communities, the risk factor indices can be communitypredictions 1120 as discussed for FIG. 11 or statistical measures basedon aggregations of information for individuals in the community (e.g., acount of positive test results, a count of individuals having infectionlikelihoods above a threshold, etc.). For individuals, the risk factorindices can be user scores 240 as discussed with respect to FIG. 3,exposure scores based on travel to locations with location tagsrepresenting exposure potential, statistical measures based onindividual data, etc.

The computer system 110 then distributes interventions and/orassessments to individuals based on the risk factor indices (step 1470).For example, if the disease exposure score for a first user exceeds athreshold, an alert or recommendation can be provided to the first user.For example, the user can be warned of the high exposure level, askedabout signs or symptoms of COVID-19, and be instructed to remain homefor a period of time. In addition, interactions can be provided to otherusers, such as those that visited a location that the first user visitedrecently (e.g., in the last 12 hours, or the last day, etc.), or moregenerally to members of the community as a whole. These interactions maybe different, for example, to ask for different types of input to bettercharacterize the risk and pattern of disease spread through thecommunity, as well as to inform and give recommendations regardingindividual exposure risks. Similarly, interventions to individuals canbe provided in response to community disease risk scores, such as actualcommunity disease measures (e.g., retrospective or current measuresbased on testing results), predicted community disease measures (e.g.,current or prospective expected measures or trends), etc. Theseinterventions can be provided selectively, for example, as communityrisk levels rise, members of the community having higher susceptibilityto COVID-19 can be identified (e.g., based on susceptibility scores 242,user information such age and underlying health conditions, etc.) andwarned first or with stricter prevention measures recommended.

FIGS. 15A-15C show examples of risk identification strategies toassessing risk through population volume, location types (e.g., businesstypes), and COVID-19 exposure events. These types of risks and otherscan be represented as location tags, e.g., records or data entriesassociated with locations, that indicate risk factors for diseasetransmission or spread that are applicable to particular areas. In manycases, these tags can done on a fine-grained scale, such as by address,by building, by portions of buildings (e.g., specific rooms or portionsof buildings), by custom areas defined with a radius or a customboundary, etc. Although the areas for which tags are applicable areillustrated as circles, they may instead have other shapes, e.g.,polygons, irregular shapes, the shape of a building or other property,etc. In FIGS. 15A-15C and 16A-16D, examples are provided for aparticular community, for which a basic map 1505 is shown to representthe locations in the community.

The location tags can be used for various purposes. One is that entryinto the corresponding area (e.g., geofence) for a location tag canprompt a notification to the user that enters the area. The figures showvarious examples of notifications that can be shown, for example, towarn a user of potential exposure risk, to inform of exposure that hasoccurred, to recommend preventative measures, etc. The notifications fordifferent figures can be provided independently or can combined withthose of other figures. Notifications can be provided by an applicationon a user device 1510, e.g., smartphone, of a user 1512 in response tothe application or the server system 110 determining that the userdevice 1510 entered the geofence of a location tag (e.g., based onuser-reported location data, responses to surveys, based on GPS data,cellular tower identifiers, detection of wireless beacon messages suchas Bluetooth advertisement messages, etc.). Along with determinations ofand notifications of previous, current, prospective, and cumulativeexposure levels or exposure risk, individuals can be providedrecommendations and other interventions determined based on thoseexposure levels, such as recommendations for disease preventionmeasures, vaccines, behavior changes, disease treatment, and so on.

Another use of the location tags is to track and report instances oractual or probable exposure to COVID-19, whether detectedcontemporaneously or after the fact. This can provide contact tracingdata that does not require communication between user devices, does notrequire human contact tracers to question individuals about theircontacts, and can capture exposure that occurs when two people visit thesame location but are not present at the same time. This type ofexposure information may be provided to individuals as well as to publichealth agencies and governments.

Another use of the location tags is to determine the overall exposurelevel or risk level at which an individual has been exposed over aperiod of time. For example, the aggregate level of exposure due todifferent tagged areas a user has entered can be determined and used toprovide personalized recommendations and treatment for a user.

Another use of the location tags is to identify the areas of highdisease transmission risk, for example, disease transmission hotspots.These can be determined by aggregating the tag data for a location,e.g., the number of tags, types of tags, and/or disease transmissionscores for the tags applicable to a location. The areas with single-tagor aggregate disease transmission scores exceeding a threshold, or a setof those that have the highest scores in a community, can be identifiedas high-risk areas for disease transmission. As another example, theactual exposure resulting at a location (as opposed to potential forexposure) can be determined by aggregating disease exposure scores forvisits of users to locations, to quantify the level of exposure thatoccurred. These disease exposure scores can also be used to identifyhotspot locations

In FIG. 15A, various areas are tagged based on population levels. Thepopulation can refer to the typical occupancy and/or traffic to thoseareas. Locations with high populations and/or high traffic (e.g., highturnover in the set of individuals present) can be determined to presenthigher risk of disease transmission. The population data can come frominformation reported by businesses, by individuals that visit thelocations (e.g., who can be asked through the application on theirdevices how many people are present at different times), census data andother public records, and so on.

The figure shows examples of various tags, where the size of the tag(e.g., the radius of the circle in this case) represents the area thatthe tag applies to, the population level, the disease transmissionscore, or a combination of these or other factors. The area canrepresent the area enclosed within a geofence for the tag. Associateddata 1515 a, 1515 b for two example tags is shown. Each location tag canbe associated with information such as average occupancy at thelocation, a traffic level at the location, a vaccination rate forindividuals at the location (and/or an immunity rate to include peoplewho have acquired immunity through contracting and recovering fromCOVID-19), and an overall disease transmission risk score due to thepopulation level and population movement patterns at the location.

The figure also shows an example of a notification 1520 a that can beprovided on the device 1510 in response to entry of the device 1510 tothe geofenced are of one of the tags having a disease transmission score(or other characteristics, such as typical number of people present)meeting a threshold or other criteria. In this case, upon entering thearea of a tag such as tag number 433, the user is reminded to wear aface mask because the user is nearing a potentially busy area.

In FIG. 15B, location types are determined for different locations, andlocations of a type that has high risk for COVID-19 transmission areassociated with exposure tags. Certain businesses or other locations arearranged in or are used in ways that lead to greater COVID-19transmission risk. The location types for location-type-based tags canbe determined based on map data, online databases, business directoriesand public records, reported data from users or business owners, etc.The example shows associated data 1516 a, 1516 b for two of these tags,such as the type of location (e.g., doctor's office, hotel, etc.) and acorresponding disease transmission score. Upon entry of the geofencearea of one of these tags, or more generally in response to proximity toone of the tagged locations, a notification 1520 b can be provided, suchas the one shown that encourages social distancing due to the presenceof a high-risk business type.

In FIG. 15C, incidents of COVID-19 exposure or potential exposure aretagged with location tags. These can represent visits by people who havebeen determined to have active COVID-19 infections through diagnostictesting. In addition, or as an alternative, these may represent visitsby people who, although not yet receiving a positive test result, haveother factors (e.g., physiological data patterns, machine learningprediction scores, etc.) indicative of high infection. In someimplementations, visit-based tags are only generated and used tocontribute to the disease exposure of others when a person has testedpositive for COVID-19, has been diagnosed with COVID-19, and/or has atleast a minimum likelihood or confidence score of being infected.Nevertheless, location tracking data can be saved for several days forall users, and if a positive test result is later received for a user,location tags can then be created based on the visits made during theprevious days, to represent the exposure the user caused even beforebeing tested.

The size, shape, location, and other properties of the geofenced areafor a visit-based location tag can be based on the properties of thevisit by the user, such as the duration of the visit, the path or areatraversed by the user during the visit, and so on. Each location tag canbe associated with information about the visit, the location, and/or theuser whose presence the tag represents. Table 1530 shows records forseveral visit-based location tags, with each row representing data abouta location tag for a different visit. Information such as a tagidentifier, location corresponding to the tag (e.g., GPS coordinates,street address, etc.), radius or other size of area affected, a diseasetransmission score, an arrival time that starts exposure, a departuretime when the user causing exposure leaves, and so on. Other informationcan be associated with a tag, such as a type of activity the userperformed, a path of movement during the visit, whether the user wore amask or took other preventative action, etc. A single location may havemultiple different visit-based tags, representing visits at differenttimes and/or by different individuals. An example notification 1520 cwarns a user that the area the user has entered a location (or is nearthe location, e.g., within a threshold level of proximity) has beentagged with COVID-19 exposure.

FIG. 16A shows locations where different individuals or groups reside.In the example, there are four individuals or groups, represented as (1)through (4). Individuals can be clustered into groups for data sharingpurposes, such as a family or a home, or for association purposes, suchas a treatment group. This allows the ability to identify and processcorrelated data within a larger set of individuals, similar to theanalysis for a sub-study within a larger research study. Relationshipswith other individuals can be exposed in a way that can help describedisproportionate risk and elevated notifications and precautionarymeasures to deliver to individuals. This can be especially useful whenindividuals have the potential to cross paths with some individuals at ahigh frequency and others at a low frequency. The computer system 110can track and identify the frequency with which different individualsand groups interact, and use this information in determining exposurerisks as well provide this information to the individuals or groups orpublic health agencies.

FIG. 16B shows an example of tracked paths travelled by the usersthrough the community over a period of time, such as a day. The pathsshown can represent paths determined through passive location tracking,active questions to users about their activities, or other techniques.The paths that users travel, and the activities that their paths anddestinations represent, can be used by the computer system 110 todetermine the exposure risks (e.g., general potential for exposure) andexposure levels (e.g., amount or frequency of actual and/or probableexposure to the disease). The paths of individuals and other locationtracking data is also used to determine the overall behavior patterns ofusers in the community generally, which can be used to compare to thebehaviors of users in other communities and determine predictions ofdisease spread, hotspot locations, and other data as discussed withrespect to FIG. 11.

For each individual, e.g., (1) through (4), the health status andexposure is tracked. For example, for users (1) and (4), no symptoms orsigns of COVID-19 are reported for the current day. For users (2) and(3), some symptoms of COVID-19 are reported.

FIG. 16C shows the overlay or combination of risk factors for populationlevels, location type, and COVID-19 exposure. This represents how therisk factors and tag types are all used together to determine individualand community risk and exposure levels. The figure shows thepopulation-based tags of FIG. 15A, the location-type tags of FIG. 15B,and the visit-based tags of FIG. 15C overlaid together on the map 1505.Note that the first two types, based on population volume and locationtype, are indicators of general risk for exposure, while the visit-basedlocation tags can represent actual or probable exposure at an area. Thenumber of these tags clustered together, as well as the individual andaggregated disease transmission scores for the tags, can show wherehotspots for disease transmission are. Two example hotspots are shown asregions 1610 and 1615.

FIG. 16D shows the combination of all of the risk tagging information aswell as the paths of travel shown in FIG. 16B. The computer system 110uses all of this information to assess the personalized exposure levelsor risk levels for the different individuals of groups, as well as todetermine personalize notifications for the users. For example, takingthe paths of travel of the individuals (1) through (4), the computersystem 110 can determine which geofences corresponding to location tagsthat each individual entered. The computer system 110 then examines thedata for the corresponding tag, and the information about the user andthe nature of the visit to that area, to determine whether the entry tothe location represents an event that justifies a notification or otherintervention (e.g., sending a testing kit, scheduling a vaccineappointment, adjusting monitoring done using passive sensing and/oractive engagement, etc.). In some implementations, certain criteriaabout the nature of the entry to a geofence may be used to filter outlow-risk visits. For example, the computer system 110 may require aminimum duration of time in the geofenced area, a minimum level ofactivity or movement in the geofenced area, a motion profile thatdiffers from certain excluded motion patterns (e.g., simply drivingthrough or walking through an area), etc.

The analysis of and notification about events that present exposure riskcan be done in real-time, e.g., in response to entry to or proximity toone of the geofenced areas and while the user is still in the geofencedarea. The analysis and notification can also be done retrospectively,e.g., at the end of the day, to assess the overall exposure and risk forthat day. Examples of notifications 1620 a-1620 d are provided, wherethe notifications inform the different individuals (1) to (4) of theirrespective COVID-19 exposure risks for the day. Exposure scores,information about specific locations or instances of exposure,recommendations about prevention of future exposure, community-leveldisease information and predictions, and other information may beprovided with these or other notifications.

As discussed above and also further below with respect to FIG. 20, thecontribution of exposure risk or exposure received from different tagscan be combined to generate an overall disease exposure score for avisit and/or for a period of time (e.g., which may include multiplevisits to different locations). The computer system 110 can retrieve thedisease transmission scores for location tags that a user entered duringa certain period of time, or other information characterizing the typeof exposure and/or level (e.g., severity, intensity, duration, etc.) ofexposure at the different locations visited. The computer system 110 canthen aggregate the different risk contributions for multiple tags, e.g.,using a summation, a weighted summation, processing by a machinelearning model, an equation of function, etc. The aggregated informationcan be used to determine an overall exposure score for a visit or a timeperiod, which can then trigger different notifications or interventions,such as recommended disease management actions (e.g., for prevention,monitoring, diagnosis, treatment, etc.).

The location tagging and disease exposure scoring of the computer system110 can be used to identify and quantify the disease transmissionpotential of different locations, e.g., disease transmission hotspots.FIG. 11 showed an example of using a predictive model 1130 c to predictwhich locations are or are likely to become hotspots of increaseddisease transmission potential. The computer system 110 can additionallyor alternatively use the aggregation of information about location tagsfor a location to determine the current level of disease transmissionrisk at the location. Comparing the aggregated measures for differentlocations allows the computer system 110 to identify hotspots having thehighest transmission potential. The aggregated measures themselves canbe individually compared to threshold or reference to determine whetherthey represent a risk level that warrants labeling as a hotspot andinitiating warnings or other notices.

In addition, the examples of aggregated disease transmission scores forlocations, (along with other information about user behaviors, locationcharacteristics, etc.), can be used as training data to train thepredictive model 1130 c. For example, the system 110 determines timeseries data indicating the location-tag-based aggregate diseasetransmission scores for a location for different times, e.g., each dayover a period of time. The disease transmission scores can be used as alabel or training target for the set of information that describes thelocation, community, user behaviors, and so on at the time correspondingto the disease transmission score. Using many instances of scores fordifferent locations having varying characteristics, the model 1130 b canbe trained to predict transmission scores for locations based oncommunity and location characteristics, allowing transmission scores tobe determined even when location tagging data is not available for alocation. Similarly, the training may incorporate a time offset betweenthe time of the input data and the time of the training target, allowingthe model to predict the disease transmission score at a time in thefuture. In addition, the examples showing instances of different orchanging risk levels, and the corresponding changes in situation thatcaused them (e.g., adding a certain disease prevention measure, removinga disease prevention measure, changes in user behavior, etc.) canincorporate into a model relationships between different conditions oractions and the effects on disease transmission risk.

In some implementations, the computer system 110 uses information aboutdifferent types of disease status in generating location tags andcorresponding information such as transmission scores. In particular,the system 110 can assign and use more classifications of disease statusthan simply whether an individual is infected (e.g., has an active caseof COVID-19) or not. The more fine-grained classifications can be usedby the system 110 to generate more accurate, personalized measures ofdisease transmission risk, infection likelihood, and so on.

As an example, COVID-19 status classifications can include whether aperson is: currently infected (e.g., active case of the disease);previously infected (e.g., has recovered from a prior case of thedisease); vaccinated against the disease; not vaccinated and notinfected; and unknown. Within these classifications, there may also besub-classifications. For example, active cases of COVID-19 may befurther classified according to the stage of the disease, such as earlystage (e.g., 0-4 days from infection), progressive stage (e.g., 5-9 daysfrom infection), peak stage (e.g., 10-14 days from infection), or lateor recovery stage (e.g., 15 days or later). Classifications may be madebased on one or more of likely number of days since infection,physiological measures, disease symptoms present, the severity ofsymptoms, or other factors.

The ability of an infected person to spread infectious particles canvary significantly over the progression of COVID-19, often being ratherhigh at the earliest stage, even before symptoms appear. The computersystem 110 can take these differences into account when assigningdisease transmission scores corresponding to location tags. For example,the disease transmission score for a visit by a person determined tohave an early stage case of the disease can be higher than the diseasetransmission score for a visit by a person determined to have a laterstage of the disease when less transmission potential is expected. Inaddition, a person determined to have an early stage infection comparedto a late stage infection may have location tags set to indicatetransmission potential for different periods of time, e.g., transmissionpotential for the previous 4 days for an early stage case, or for theprior 10 days for an intermediate stage case. As another example, theamount of viral shedding may correspond to the type and severity ofsymptoms, and so the disease transmission score can be based on thesymptoms, or symptom classification, of the user. In addition, thedifference in disease transmission potential may vary for reasons otherthan biological effects. For example, people with early stage infectionsmay be more active and less careful than those with later stage cases,who are more aware of their risks and whose activities are restraineddue to disease symptoms.

The computer system 110 can estimate the differences in potential fordisease transmission for different stages or symptom profiles using thedata collected for many cases of infection and subsequent spread. Forexample, contact tracing data and data generated from location taggingand geofencing can provide examples of how likely people havingdifferent disease stages are to spread the disease to others. Withtechniques such as statistical analysis or machine learning, thecomputer system 110 can determine the differences in diseasetransmission for different disease case characteristics (e.g., symptomspresent, time since infection, type of treatments being administered,patient attributes, etc.). For example, the computer system 110 canexamine the rate at which different location tags representing visits bydifferent types of disease cases result in new infections, and thenadjust the disease transmission scores for later-generated location tagswith the relationships learned. As another example, the computer system110 can use clustering algorithms, such as K-means clustering, to groupdata sets by similarities, and then use the clusters as indications ofcombinations of factors (e.g., disease stage, symptoms, patientcharacteristics, etc.) that represent different levels of transmissionrisk. In addition or as alternative, the computer system 110 can receiveand use third-party research or measures that specify different levelsof disease transmission potential for different

In addition, vaccinated individuals may be further classified by thetype of vaccine used, number of vaccine courses received, whethervaccination is complete or partial (e.g., whether courses are stillremaining), an amount of time since vaccination (e.g., less than 1month, 1-6 months, 6 months to a year, greater than a year, etc.).Vaccination status and prior infection status can have a large impact ona person's ability to become infected and to infect others. Thusvaccination status can be used to generate predictions such as a user'ssusceptibility to the disease and the user's current likelihood ofinfection. These factors are also used to calculate the potential of auser to infect others, e.g., as specified by the disease transmissionscore for a visit-based location tag. The protective effect ofvaccination or a prior case of the disease may decline over time,however, and so the amount of time elapsed since infection or recoverycan be a factor in determining the susceptibility of an individual, thecurrent infection likelihood, and the disease transmission score for avisit to a location.

In some implementations, the computer system 110 can store and useinformation indicating whether individuals have been vaccinated for adisease, such as COVID-19. Vaccination status can be obtained from, forexample, electronic medical records, insurance claims data, or fromasking individuals in a survey or other interaction through theapplication on the user device. Location tracking data can then showwhere people are at different times, allowing the system 110 todetermine, for different locations and at different times, thepercentage of people who are vaccinated. Not all people present at alocation are likely to be participating in the data collection andtracking program, and not all people will have a suitable device, orhave it powered on, etc. Nevertheless, the tracking data that isavailable can be used as a sampling of the population present at alocation, and the percentage among those tracked can still be a valuableestimate for the percentage at the location generally. Vaccinationstatistics for a community as a whole may also be obtained, such as frompublic health agencies.

The vaccination rate at different communities and for differentlocations within a community can be used to customize the diseaseexposure levels calculated. For example, a population-based location tagfor a location where 80% of people present are vaccinated will present amuch lower risk than a population-based location tag for a location witha similar population density but only a 30% vaccination rate.Accordingly, the vaccination rate at a location that a user visits canbe used to weight or scale (e.g., to decrease) the contribution ofpopulation-based location tags to a user's disease exposure score. Insome implementations, location tags can be associated with datavaccination rate data for the location, e.g., a vaccination ratemeasured for a certain day, week, or other time associated with a tag.In other implementations, a dynamic, time-varying vaccination rate isdetermined, e.g., based on the variation in the sets of people comingand going to result in different vaccination rates at different timeswithin a single day. For example, for calculating the exposure riskcontribution of a specific visit at a specific time, the computer system110 can use the vaccination rate for the location at the time of thevisit or for a time window including the visit (e.g., a 2-hour window inwhich the user visited the location).

FIG. 17 shows an example of traffic data that the computer system 110can use for analyzing traffic patterns for communities. In addition toassessing travel within communities, the computer system 110 can analyzetravel of individuals between communities and how it impacts the spreadof disease and the effectiveness of different disease preventionmeasures. The example focuses on travel patterns relating to the city ofPocatello, Id. The figure shows a map 1710 with different areas 1712,1714 showing different levels of traffic in the area around the city.The map 1710 also shows different travel paths 1720, 1721, 1722 betweendifferent cities. These areas 1712, 1714 and paths 1720, 1721, 1722represent an aggregation of tracked location data (e.g., tracked throughsensor data capture, such as GPS, and/or through user-reported data)over a period of time such as a day, a week, a month, etc. The differentlevels of traffic for different areas and different paths representsdifferent levels of connection or disease transmission risk amongdifferent areas.

The computer system 110 can use the travel information to calculate orpredict how disease measures in one region or community will affect thedisease measures in another community. For example, the disease measurepredictions for Pocatello can be adjusted based on the disease measuresfor Medford and Seattle based on observed travel patterns from theselocations to Pocatello. Nevertheless the level of influence that diseasemeasures of Medford and Seattle have different levels of influence ondisease measures for Pocatello, due to the different amount of travel toand from these cities with Pocatello. In other words, travel betweenMedford and Pocatello is more frequent or more regular than betweenSeattle and Pocatello, so Medford's disease measures impact those ofPocatello more than do the disease measures of Seattle.

FIG. 18 shows an example of a user interface showing sample data of amap that can be provided to a user, such as a member of a community, abusiness owner, a government worker, a member of a public health agency,etc. As shown, hotspots of disease transmission 1810 or diseaseprevalence can be indicated, along with different affected businesses orother locations, as well as disease prevention recommendations.

In addition to the tools discussed above, the computer system 110provides support through a suite of individual and community managementtools. These can be provided to contact tracing staff, communities andbusinesses, health care providers, public health agencies, governments,and other individuals and organizations. These tools can be used tomanage communication with individuals and data access and sharing.

The computer system 110 can provide a population data management tool,such as user interfaces (e.g., in an application, web application, webpage, etc.) or application programming interfaces (APIs) that supportadding or retrieving population data for communities and locations.Various techniques can be used to request, track, and update populationdata, including prompts to users and responses, passive tracking, censusinformation, and so on.

The computer system 110 can provide COVID-19 community exposure datamanagement. This can include providing user interfaces and APIs thatenable adding and retrieving information related to COVID-19 forcommunities, such as information about disease exposure or transmissionevents, disease cases (e.g., confirmed, probable, active, resolved,etc.), treatments applied or ongoing, hospitalizations, and deaths.

The computer system 110 can provide location classification and businesscontext data management. The computer system 110 can provide userinterfaces and APIs for managing building information andtraffic-related information. This can include entering and retrievinginformation about locations, such as a number of individuals allowed(e.g., maximum occupancy), typical occupancy, business size, operatinghours, environment, air conditioning, outdoor seating capacity, and typeof business.

The computer system 110 can provide adjustment of tag data (e.g., toimplement the aging or progressive decrease in impact of visit-basedlocation tags over time) and path tracking data management. The computersystem 110 can provide user interfaces and APIs for receiving andproviding information about factors that affect the progression by whichthe influence of location tags decreases over time. Examples of theseconditions include conditions at the location of a location tag,conditions at surrounding areas, whether the location an open area or anenclosed space, a level of airflow or ventilation, environmentalinformation such as humidity and moisture levels, sanitizationprocedures at the location, and so on. Due to variation in thecharacteristics and conditions at different locations, the decrease inimpact of an exposure event can vary for different locations. Forexample, location tags for different locations may respectively persistfor 48 hours, 16 hours, 1 hour, or other lengths of time, depending onthe environment at the location and characteristics of buildings at thelocation. The location tag no longer contributes to the exposure risk ofa person visiting the location after the end of the persistence period.The disease risk presented over the persistence period of a tag may alsodiminish according to a pattern, e.g., a linear decrease, an exponentialdecrease, etc.

The computer system 110 can provide notification messages and alertmanagement. The computer system 110 can enable support for generatingand sending various types of messages through the application on users'devices or through other communication channels. The types of messagescan include predictive messages (e.g., indicating predictions for anindividual or the individual's community), risk notifications (e.g.,alerting users to contact with a person that has contracted COVID-19 oran area where an infected person is or has been), messages regardinganticipated future risks, messages educating or instructing users ofdisease prevention measures (e.g., face masks, social distancing, etc.),warning users of current or future exposure risks, informing users ofchanges in risk levels (e.g., for the individual and/or for theindividual's community). In many cases, the system 110 is configured toautomatically alert or educate users about the risks occurring fromassociated interactions with specific individuals, with otherindividuals or groups of potentially unidentified individuals, and inthe community generally.

The computer system 110 can provide case management tools. The computersystem 110 can provide user interfaces and APIs for managing appointmentscheduling for individuals and health care systems. The system 110 canfacilitate management of healthcare staff schedules, automaticallyassign specific cases to staff members to promote efficient staffmanagement, provide additional information about individuals whenavailable from data from previous interviews, and provide elements toescalate a case to emergency services in situations where intervieweesrequire immediate assistance. Additionally, the system can provide alogical model for communicating with participants, where the state of aninterview or evaluation process can be saved, so that interactions canbe seamlessly restarted if a contact (e.g., someone identified as havingor likely to have had contact with a person with a case of COVID-19) islater identified to present with a case of COVID-19, or if a case isreopened for repeated infection.

The computer system 110 can provide consent management tools.Individuals may be required to provide consent in order to participatein a research study, community health initiative, information gatheringprogram, disease monitoring or treatment program, etc. The computersystem 110 can provide user interfaces and APIs for individuals toprovide consent through a digital interface (e.g., via email,application, website, or SMS text message) or via phone engagements.

The computer system 110 can provide configurable identity protection. Asworkers collect sensitive personal identifying information (PII), thesystem 100 reduces unnecessary exposure of staff to PII by restrictingthe number of situations where PII of contacts are exposed tointerviewers.

The computer system 110 can provide contact deduplication. As contactsare reported by multiple COVID-19 positive interviewees, the systemensures that contacts who are already assessed are not scheduled forunnecessary repeated assessments.

The computer system 110 can provide contact tracing script management.As public health staff begin contact tracing by notifying exposedindividuals of their potential exposure as quickly and sensitively aspossible, the system 110 improves the quality and efficiency of theseengagements.

The computer system 110 can calculate dates for disease onset andquarantine periods of time. This can assist contact tracing staff andinterviewees so they do not need to calculate onset dates orrecommendations for quarantine periods. The calculation of disease onsetdates can be based on the symptoms reported by users, physiological andbehavioral data collected through passive sensing, incidents ofinteraction with other individuals and locations presenting exposurerisks. For example, the location tracking history for a user andgeofence entry information can indicate one or more likely times when aperson came into contact with the SARS-COV-2 virus. In addition, thetiming and pattern of symptoms the user reports (e.g., breathingdifficulty, headache, etc.), or which are reflected in physiologicalmonitoring data (e.g., temperature, heart rate, heart rate variability(HRV), etc.), can be indicative of the stage of disease at the times thesymptoms and physiological measurements were reported. Using referencedata (e.g., patterns, guidelines, models, etc.) that aligns symptomprogressions and physiological changes to disease states, the system canalign the individual's pattern of symptom progression with that of thereference data to estimate a time of infection. When contact tracingdata or potential exposure event data is available, the comparison withreference data can be used to select which of different exposure eventsmost likely led to the infection. The reference data can be learned fromtraining data for disease progression for many different people, e.g.,through statistical analysis, machine learning, etc.

The computer system 110 can provide scripts (e.g., a series of prompts,guided dialogues, questions, etc.) to assist in recalling any potentialclose contacts during time periods where they have been infectious,which can assist recall and improve the quality of interaction. Thecomputer system 110 can customize scripts that limit unnecessaryquestions to contacts, using adaptive logic to ask comprehensivequestions only when higher order questions identify the need. Forexample, the default script can include an initial set of basicquestions each targeted to a different subject area, and when a userprovides a response indicating a risk or uncertainty, the system canexpand the set of questions to ask more detailed questions in thecorresponding area. Similarly, when available data indicates that datais already collected is sufficient to rule out a risk or meets acollection need, further questions can be skipped. Scripts can be sharedwith other agencies, teams, and processes to improve standardization fordata collection and ensure expedient convergence on common data elementsas yet unpublished within the hub/spoke model. Forms are designed toincrease the speed of data entry to limit the inconvenience tointerviewees and improve interviewer efficiency. Scripts can take intoaccount the regional guidance of local participant contexts to ensureconsistency with each jurisdiction. The computer system 110 canfacilitate collection of preferred contact information across multiplemodes of communication including phone, email, SMS, and mobileapplication push notification, where possible. The computer system 110can provide direct support for installation of contact tracing mobileapplications and for distribution of digital health wearables forparticipants who consent to this data collection. The computer system110 can provide proximity reporting tools that allow for interviewees toreport information such as a dates, times, physical locations, andcharacteristics of exposures.

The computer system 110 can provide tools for privacy management.Disease cases, contacts tracked by the geofencing and contract tracingsystem, and any other data collected or generated can be associated with(e.g., linked to or annotated with) information about how data iscollected, how long it is stored, and how it is to be used.Additionally, if requested, the computer system 110 can provide securelogin capability to participants, to enable individuals to update theirinformation at a later date (e.g., after initial enrollment andconsent). Similarly the system can provide interfaces or functionalityallowing participants to delete their personally identifying informationand request no further contact. Additionally, the computer system 110can be assessed by a publicly-accessible auditing process to ensure thatparticipants can be provided evidence of trustworthiness, security, andprivacy. In some implementations, access to collected data is limited toresearch staff (e.g., study staff working with data for a cohort ofindividuals in a research study) and public health authorities in keyjurisdictions, and can be restricted by role or be available on aneed-to-know basis only.

The computer system 110 can provide tools for contact assistance. Thecomputer system 110 can provide tailored educational materials to engagewith both COVID-19-positive interviewees and individuals who may havebeen in contact with them. The materials can be directly provided viain-application notifications, device notifications, text messages,email, or paper mailed directly to participants directly. Additionally,these materials can be made available on a public website, can be usedby interviewers in direct conversations as necessary, and can beprovided in mobile applications for participants with eligible devices.The materials are updated in real-time as new data emerges, so thatcontact tracing personnel do not need to be continuously educated. Thismulti-modal approach enables staff to expediently transmit informationefficiently (e.g., in less than 30 seconds, in a single button press, totransmit information to a de-identifed person). For example, thecomputer system 110 can provide different types of messages orinteractive forms, which a user can select among so that the message orinteraction can be provided to a specific individual or a group ofindividuals, potentially to a whole community. The messages can includecontent to: inform individuals of their risk; instruct individuals toseparate themselves from others who are not exposed; monitor themselvesfor COVID-19, inform individuals about transmission risks to others evenwhen non-symptomatic; provide valuable, tailored, self-quarantinesupport that encourages individuals in contact with an infected personto stay home, monitor their physical health, maintain social distance,and provides guidance on the safest way to procure necessary supplies;provide treatment articles, equipment, or medication; provide supportmaterials for mental health issues encountered during a quarantineperiod due to contact with infected person or generally.

In addition to or instead of providing content for users to manuallyselect and send to others, the computer system 110 can include mappingdata that associates different conditions with corresponding messages orinteractions to send to users whose situations meets the conditions. Thecomputer system 110 can evaluate collected data for individuals on anongoing basis, detect when one of the conditions is present, andautomatically send to a user a message of the type that the mapping dataindicates as corresponding to the condition. For example, the mappingdata may indicate that a message informing a user of exposure risk andan instruction to self-quarantine for 14 days should be provided when auser is exposed to COVID-19. When an exposure event is detected, such asusing location tracking and geofence data to identify that a personvisited a location at the same time or shortly after an infected person,the corresponding message can be automatically selected and set to theuser's device for presentation. The conditions for sending messages orinteractions can be based on one or more of the various types of datacollected and generated by the computer system 110, includingpredictions for individuals, predictions for the community of anindividual, physiological data, behavior data, sensor data, userinteraction data or user input data, medical records, and so on.

FIG. 19 is a flow diagram showing an example of a process 1900 ofgenerating disease-related predictions for a community. The process 1900can be performed by one or more computers, such as the computer system110. In general, the operations of the process 1900 can be performed byone or more servers, one or more client devices, or a combination ofthem. In addition to the operations illustrated, the process 1900 canoptionally include other operations discussed with respect to FIG. 11.In the example, the disease mentioned can be COVID-19, or it can beanother infectious disease.

The process 1900 can be used to leverage the rich data set that thesystem 110 collects about individuals in a community to provide varioustypes of information for the community. As discussed above, thepredictions and recommendations about the community can be used toprovide better disease monitoring, diagnosis, testing, and treatment forindividuals in the community.

As discussed above, the system 110 can collect information aboutindividuals such as physiological data, behavior data, medical records(e.g., EHR/EMR), sensor data from mobile devices and medical devices,and more. The system 110 can also obtain information about individualcommunities, such as demographic data, map data showing the locationsand location types in the community, and disease measures in thecommunity. With the community data and the individual data for membersof the community, the system 110 can make predictions andrecommendations that are tailored for the characteristics of thecommunity. For example, the data about individuals can be used to bettercharacterize and predict the current and future disease measures for thecommunity, the risks and impacts of the disease on the community, thecurrent and future “hotspots” of disease transmission, and preventativemeasures most likely to be effective for a particular community, e.g.,taking into account the actual behavior patterns of members of thecommunity, the geography of the community, and other community-specificfactors. By tracking behavior of members of the community, and bydetecting responses or behavior changes of members of the community overtime (e.g., as social distancing policies change, as public healthinterests increase and decrease, etc.), the system 110 can provideaccurate and useful predictions and recommendations that are customizedfor actual conditions in a community.

As one example of the types of predictions that can be provided, theprocess 1900 can be used to identify or predict regions of high diseasetransmission potential. The training of the machine learning modelsallows the system 110 to make predictions about current and/or futurehotspots of disease transmission, e.g., regions where high or increasedlevels of disease transmission are likely to occur. Importantly, thepredictions can be determined based on behavior data for individuals(e.g., location tracking data, self-reported location data, activitydata, etc.), which can show the actual, current behavior patterns ofpeople with respect to the locations being evaluated. This can oftenallow very timely, up-to-date predictions, for example, with same-dayresults or even real-time updates as monitoring data is received. Thepredictions can be based on information about specific locations (e.g.,the business type, occupancy measurements or patterns, type of buildingor physical conditions, etc.). The predictions can also take intoaccount other factors such as community disease measures, diseaseprevention measures in place, and more.

Unlike many systems, the hotspot identification can be based on morethan simply tracking where past transmissions occurred. While contracttracing data and data indicating actual disease transmission events ishelpful and can be used, that data is often a lagging indicator, oftennot available until days after transmission occurs and the transmissionrisk of the location is elevated. It also depends on rapid, widespreadaccurate disease testing and significant investment in contact tracing,both of which are not always available. Similarly, location type is asignificant factor but on its own does not account for the differencesof each location and actual use of the locations, and so may not be anaccurate measure of disease transmission risk. For example, twolocations may both be classified as grocery stores, but may have vastlydifferent disease transmission potential due to factors such asdifferent levels of traffic, different physical layouts resulting indifferent in-store proximity, different proximity to population centers,and so on.

The present techniques allow for more accurate predictions customizedfor the characteristics of a community, for disease transmissionhotspots and other predicted items. For example, the use of actualbehavior patterns for individuals in a community and the characteristicsof individual locations allows higher accuracy. As another example, thepredictions can take into account the disease preventions measures(e.g., masks, social distancing, occupancy reductions, etc.) that are inplace and the compliance level for those measures (e.g., the percentageof people actually wearing face masks when recommended or required). Thepresent system can use additional or different factors as inputs toidentify and characterize hotspots, often well before traditionalretrospective-focused techniques indicate a potential risk. As a result,the present system provides an improved technique to identify (i)regions are currently regions of increased disease transmissionpotential for a particular disease, such as COVID-19, as well as (ii)regions that are predicted or expected to become regions of increaseddisease transmission in the future. The models can be used to generatescores that quantify or classify the levels and types of risks presentedby different regions.

In many cases, the system 110 also allows predictions of hotspotswithout requiring contact tracing between individuals. Optionally,contact tracing data can be used as an input to the process whenavailable. Also, contact tracing data, if available, can be used totrain machine learning models. Nevertheless, even without contacttracing data, and for communities where contact tracing is not robust orreliable, the machine learning models and other techniques can use thelearned relationships between behavior patterns and other data (e.g.,location characteristics, community disease levels, communitydemographics, geography, etc.) to predict how a disease may spread inthe community and the extent or risk of spread at specific locations.

The process 1900 includes collecting data for individuals in a community(step 1902). Various users in a community can have a softwareapplication installed on their mobile devices, e.g., cellular phones,which facilitates sensing of data on an ongoing basis as well asinteractions with the user. The application can request information froma user, through interactive forms, surveys, ecological momentaryassessments (EMAs), etc., and provide the responses to the computersystem 110. The application can also facilitate monitoring of userbehavior, such as through capture of sensor data. The data collectioncan obtain any of various types of information about a user, such asbehavior data (e.g., data indicating location, movement, activity ortask being performed, etc.), physiological data, mood and mental healthdata, and more. Data collected for a user can be associated with a useridentifier for the user, so that the computer system 110 can organizeand group the data received for each individual.

Collected data for individuals monitoring using sensors of a device of auser, such as sensors of a phone, smartwatch or other wearable device,medical device, or other device. This can include collecting data usingwirelessly connected devices, such as a pulse oximeter, digitalthermometer, weight scale, or other device that provides sensormeasurements to a user's mobile device. This monitoring can includetracking the locations of individuals, using a GPS receiver or receivedsignals from cellular base stations, Wi-Fi access points, Bluetoothbeacons, other mobile devices, etc.

Collected data for individuals can also include user interaction data,such as responses to questions and other prompts provided by anapplication at the user's mobile device. For example, surveys and EMAscan ask users about the user's health and current situation, andresponses are collected and reported to the computer system 110 over anetwork. The computer system 110 may send instructions or contentcausing mobile device applications to prompt users, such as to askcertain questions or request certain types of data from users. Theinformation request of users can include, among other items, informationdescribing a user's current location, conditions or context at thecurrent location, a current activity of the user, the user's plans forsubsequent actions, the user's health status, physiological measurementsfor the user, and so on.

Collected data for individuals can also be acquired from other sources,such as electronic health records (EHR/EMR), a device of a physician orhealth care provider for a user, devices of friends or family of a user(e.g., who can be prompted regarding the user), etc.

The process 1900 includes collecting data for a community (step 1904).The computer system 110 can be designed to collect data for, and providepredictions and recommendations for, many different communities. Forclarity, the process 1900 emphasizes actions for one community, but theprocess 1900 may be repeated for any of many communities that thecomputer system 110 is configured to monitor and assist.

As discussed above, a community can represent a group of people. Forexample, a community can be defined as a group of people associated witha predetermined geographical area, such as a state or province, acounty, a city, a zip code, a neighborhood, etc. The community may bethe group of people that reside in the area, the group of people thatwork in the area, a group of people that work or reside or otherwisevisit the area, etc. The computer system 110 can store data that definesand describes different communities. The stored data can associate aunique community identifier for each community and data describing thecriteria for membership in the community, e.g., geographical boundariesof the community and other features of the community. A community may bedefined independent of a geographic area, for example, such as themembers of a certain organization, individuals in a certain profession,a set of people that have visited a geographical area within a certaintime (not necessarily permanently residing in that area), etc. Invarious examples below, a community refers to the individuals in thecommunity can be those that reside in the corresponding geographicalarea. Different communities can refer to different geographic areas andthe people who reside in them, for example, different cities, counties,neighborhoods, zip codes/postal codes, etc.

Examples of the community data that can be collected for the communityinclude map data or geographical data, community characteristics,community policies, and community disease measures. Any or all of thesetypes of community data can be determined for a community to make aprediction for the community, as well as for other communities so thecommunity data can be used as training data for predictive models. Thisinformation can be requested and received from public databases, mapdata services, and other sources. The map data or geographical data caninclude data indicating the locations in the community, their locationtypes (e.g., business types, such as grocery store, shopping mall,library, etc.), the distances and spatial relationships of locations,roads, and other geographical aspects of the community. The communitycharacteristics can describe properties of a community for differentdimensions or aspects, such as population size, population density,demographic characteristics (e.g., population measures by age, sex,race, ethnicity, economic status, income, education level, etc.),urbanicity (e.g., a degree to which the community is urban),classifications for the community (e.g., urban, suburban, or rural),prominent industries in the community, percentage of residential vs.commercial space, and so on. The community characteristics can includecharacteristics of population structure for the community, such aspopulation size, geographic distribution of the population, andcomposition of the population. The community characteristics can alsodescribe measured of demographic processes for a community, such asmeasures of fertility, mortality, and migration. The community policiescan indicate rules or regulations of the community, including diseasepreventions measures that may be recommended or required (e.g., socialdistancing of a certain distance between individuals, stay-at-homeisolation, wearing of face masks, etc.). Community disease measures caninclude, for example, indicators for a quantity of cases of a disease,hospitalizations for the disease, deaths from the disease, rates orpercentages of positive and negative tests for the disease, etc.Measures for confirmed and/or probable cases can be collected.

The community disease measures can be provided in various forms, such ascumulative values over time, values for individual time periods (such asa most recent day or week), time series indicating different values fordifferent times, etc. The community disease measures can data providedby public health agencies at the national and/or local levels, e.g.,data from the Center for Disease Control (CDC), U.S. Department ofHealth and Human Services (HHS), state-level public health departments,etc. Community disease measures can also be provided from hospitals,clinics, physicians and other sources. In some cases, the computersystem 110 may compile some community disease measures from thecollected data from individuals, such as self-reported data and healthrecord data that may indicate when users began showing symptoms of thedisease and when and if positive disease test results were obtained.

In general, the data collection steps 1902 and 1904 are performedrepeatedly, e.g., continually in an ongoing manner. For example, newinformation can received, and in some cases requested, hourly, daily, orat another appropriate frequency. In some implementations, themonitoring data and user interaction data for many different individualscan be provided as a data feed or data stream collecting reports insubstantially real time as the data is collected.

The process 1906 includes accessing one or more predictive modelstrained based on disease outcomes in multiple communities (step 1906).For example, the a predictive model can be trained based on trainingdata that describes (i) a plurality of different communities (e.g.,includes community data for the communities as discussed above) and (ii)behavior patterns and disease outcomes of individuals in the differentcommunities over time. The model can thus be one that has been trained,based on the relationships indicted by the training data examples, toindicate how characteristics of a community and behavior patterns ofmembers of the community affect the disease outcomes (e.g., geographicalspread or distribution of disease cases, number of disease cases, rateof change of disease cases, severity of symptoms, number ofhospitalizations, number of deaths, community disease measures, etc.)for a disease. The data describing the community can be any of theaspects of community data discussed above (e.g., communitycharacteristics, community policies, community disease measures, etc.).

The data that describes behavior patterns can indicate behaviors ofindividuals separately or show an aggregate measure of how individualsin the community act. The behavior data can describe, for example:characteristics of travel within or outside of the community (e.g., asdetermined from location tracking data, self-reported locationinformation, etc.), including modes of transportation used, commondestinations or types of destinations, and so on; activities performedby members of the community (e.g., jogging, shopping, attending movies,working, etc.); sleep behaviors; diet, such as what users are eating,how much, and when; exercise levels and types of exercise; medicaltreatments and appointments (e.g., what treatment steps are taken forthe disease or other disease, whether they are visiting the doctor andhow often, etc.); and so on. For all of these types of behavior, thedata can indicate characteristics such as the frequency, duration,timing, level of consistency or regularity, etc. of the differentbehaviors. When training a predictive model, the computer system 110 maytake records of behavior of many individuals, and determine from thoserecords the patterns or behavioral characteristics that are most commonor most representative of the community, then use the patterns asfeatures provided to the model. Similarly the input to the model may befeatures representing aggregate measures for different behavioralaspects, e.g., an average number of hours of sleep per night for membersin the community, an average level of exercise (e.g., step count, mileswalked, etc.), an average number of public areas visited daily or anamount of time spent in public areas daily, etc. More complexdistributions showing the variation in behavior measures among membersof a community and over time may additionally or alternatively be used.

The training data for the model can indicate disease outcomes forindividuals and their corresponding disease outcomes of the individuals(e.g., whether the individual became infected, what symptoms werereported, the duration of disease symptoms, whether the individual washospitalized, needed ventilator support, or died, etc.) In particular,the models can be trained with data about the disease outcomes ofindividuals and/or communities over time, in other words, the patterns,changes, and progressions of community disease measures over time.Similarly, the training data can indicate behavior patterns ofindividuals over time, allowing the model to learn how different typesof changes in behavior of people in different types of communitiesaffects the changes in disease outcomes.

As discussed with respect to FIG. 11, the computer system 110 can storevarious community prediction models 1130 (e.g., models 1130 a-1130 d)that are trained to make various predictions 1120 current and futureitems for a community. These can be machine learning models that havebeen trained based on examples of a diverse set of communities andbehavior patterns and the corresponding infection rates for the diseaseor another disease.

As an example, the computer system 110 can capture data describingvarious communities, the behaviors patterns for individuals in thosecommunities, and community disease measures showing the spread ofCOVID-19 in those communities over time. With these examples, thecomputer system 110 can train a machine learning model, such as a neuralnetwork, to predict any of various results of interest. This can includepredicting the disease outcomes for a community (e.g., rate and extentof the disease spreading) either at the present time or at a future time(e.g., a predetermined time such as a day, a week, or a month in thefuture. Another prediction that the models can be trained to provide isan expected impact of the disease, e.g., a speed, magnitude, or extentof change to economic indicators, hospital utilization and otherinfrastructure indicators, and other outcomes in the community.

The training data can include explicit or implicit data that indicates,for each of the training examples, the type of measure the model isbeing trained to predict. For example, if a model is trained to predictfuture disease measures a week later, the training examples can includea dataset showing a “snapshot” of the state of communities at certaintimes, each with the corresponding disease measures a week later. Inother cases, the model may learn from implicit outcomes, such astraining data providing example data sets that show examples ofcommunities, changes in disease prevention measures, and resultingdisease outcomes. While these do not explicitly specify which diseaseprevention measure was best for each community at each time, thetraining process can nevertheless teach a model to prefer or scorehigher disease prevention measures that produced better communitydisease measures in different situations. With a cost function orobjective function structured to do this, a model can be trained toreceive community data and (i) output a prediction of the communitydisease measures that would produce the best results, and/or (ii) scorethe expected effectiveness or results of different community diseasemeasures, given the patterns reflected in the training data.

As another example, the computer system 110 can use the collected datato train a model to evaluate the infection transmission risks presentedby different locations and behavior patterns. The computer system 110can use the tracked and/or self-reported behaviors individuals (e.g.,indicating items such as locations and activities of individuals) andtheir subsequent disease status (e.g., symptoms, infection likelihoodscores, disease testing results, etc.) as examples. The data sets usedin training can indicate the nature of a user's visits to differentlocations, such as activity performed, duration of visits, conditions atthe time (e.g., level of traffic or occupancy at a location, whetherface masks were worn, prevalence of the disease in the community at thetime of the visit, etc.) and other behavior information. The manyexamples showing user interactions at different locations and subsequentoutcomes (e.g., whether users contracted the disease or not) can be usedto estimate the levels of disease transmission risk for differentlocations.

As an example, the system 110 can determine a correlation between visitsto different locations and subsequent contraction of the disease byindividuals that visited the location. The system 110 can furtherdetermine the correlation between contracting the disease and thedifferent properties of a location, a community, and user behavior. Forexample, the model may learn and incorporate into its training thatsmaller convenience stores with high traffic and longer-than-averagevisit durations may have elevated disease transmission potential. Asanother example, the training may incorporate that, based on theexample, sporting goods stores have high transmission potential when onemovement profile or in-store behavior profile is detected, but have lowtransmission potential when a different set of behaviors is typical.Thus, a model can be trained to predict the disease transmission risk ordisease transmission potential for a location given the location'scharacteristics and the characteristics of the surrounding community andthe behaviors and characteristics of members of the community.

Note that in addition to or as an alternative to training based onpatterns of individual behavior and individual disease measures, theaggregate behavior patterns of individuals across a community can beused. For example, the numbers of people at different stores orlocations and the correlation with changes in disease measures canindicate which locations have greatest potential for transmission. Apublic park may show an great increase in the number of people visitingin a day or number of people present at a time, but there may be nocorresponding increase in disease transmission over time, which canteach the model that the park is not a high risk for diseasetransmission. On the other hand, if examples show that grocery storesreducing their hours or occupancy are followed by resulting communitydisease measures that indicate reduced rates of disease spread, this canshow (accounting for other variables and conditions in the community)that the grocery stores are likely areas of disease transmission andthat the changes in use of the location were an effective diseasetransmission measure.

The process 1900 includes generating one or more disease-relatedpredictions for the community using the one or more predictive models(step 1908). For any of the predictions, one or more predictive modelstrained to predict that type of output can be used. The model can beprovided a set of input data, such as a feature vector including featurevalues for each of various types of data that the model was trained toprocess during training. The input data can include values indicative ofcharacteristics of the community and of individuals in the community.Data regarding individuals provided as input to the models can beprovided in an aggregated form to represent typical or general patternsof behavior or user attributes, through aggregations such as averages,minimum and maximum values, etc.

The input values for the predictive models can be tailored to the levelof prediction needed. For example, to evaluate the disease transmissionpotential of a particular location (e.g., a specific store in acommunity), data indicating the characteristics of the store andbehavior patterns at that particular location can be provided, inaddition to or instead of community-level features. A model trained forthe purpose would receive the same types of data in training to be ableto process this type of data. As another example, a model may receivecommunity-level characteristics and behavior patterns to evaluate theeffectiveness of different disease prevention measures for the communitygenerally. Nevertheless, a different model may be trained and configuredto receive location-specific characteristics and behavior patterns forindividual locations (e.g., often along with community-levelcharacteristics and behavior patterns), in order to provide scoresindicating the effectiveness or suitability of different diseaseprevention measures for each location individually.

The predictions can be of any of various types. For example, a model canbe used to generate one or more scores 1909 a indicating a predictedimpact of the disease on a community. As another example, a model may beused to provide output indicating one or more regions 1909 b predictedto have elevated disease potential. For example, a neural network orother model can be trained to receive information about a location, andoutput a disease transmission score indicating a level of risk that thatlocation poses for transmitting the disease. The input can indicatecurrent or typical attributes for the community and for behaviorpatterns of users at the location, allowing the prediction to betailored for the specific conditions at the specific location at aspecific time—including the way user are monitored to be behaving withrespect to the location. The output for the model can be a diseasetransmission score for a location, where the score quantifies a risklevel for or predicted level of disease transmission at the location,given the characteristics of the location, the characteristics anddisease prevalence in the community, and the behaviors of individuals inthe community and/or at the location. The computer system 110 can thenevaluate the disease transmission scores for different locations bycomparing them with a predetermined threshold. Disease transmissionscores having values that exceed a threshold (e.g., indicating diseasetransmission risk above a certain level) can be identified as diseasetransmission hotspots. As another example, a model may be used toprovide one or more predicted disease measures 1909 c for the community.As another example, a model may be used to provide output indicating oneor more disease prevention measures 1909 d for the community. Forexample, this may be provided as effectiveness scores that indicate, foreach of one or more different disease prevention measures, how effectivethe disease prevention measure is expected to be given the trainingexamples observed during training. In other words, the effectivenessscore can indicate how well a disease prevention measure is expected toreduce disease transmission, given the training data showing effects ofthat disease prevention measure being used in other communities orlocations having similar characteristics, user behavior patterns, andother attributes similar to the current community or location for whicha prediction is being made.

The process 1900 includes providing output indicating the one or moredisease-related predictions for the community (step 1910). For example,the predictions can be provided to individuals that live in thecommunity, to warn them of disease transmission hotspots, to indicatewhich disease preventions measures are most likely to be effective intheir community or in the locations they visit, or to inform them of thepotential impact of the disease or the predicted future disease metricsand trends predicted. This can be provide through the softwareapplication used to collect data for individuals, or through otherchannels (e.g., email, text messages, etc.). As another example, thepredictions can be provided to businesses, governments, health agencies,hospitals, and other organizations. For example, predictions regardingdisease impact and predicted disease measures can help with planning, byindicating the urgency of further disease prevention measures or showprogress in controlling the disease. Similarly, the predictedeffectiveness of different disease prevention measures can aid in makingpolicy decisions, such as determining which disease prevention measuresto encourage or require, the extent that businesses should open oroperate, etc. The predictions can also be provided to hospitals anddoctors to help them prepare for potential imminent increases in demandfor resources, such as needs for increased disease testing, hospitalbeds, etc.

The process 1900 includes adjusting disease management for individualsbased on the disease-related predictions for the community (step 1912).This can include selecting or adjusting monitoring, detection,prevention, testing, and/or treatment for the disease for an individual.For example, a prediction of a future a community disease measure mayindicate an increase in disease prevalence or a location where a userworks may be predicted to be (or be likely to become) a diseasetransmission hotspot. In response, the computer system 110 can adjustthe personal predictions for an individual in the community, and cantake steps to more aggressively encourage avoidance of disease exposure,and/or to increase the level of monitoring, testing, and treatment forthe disease for the individual. This can include selecting andrecommending a mediation or vaccine for the user, and/or to initiate orchange the intensity of digital therapeutics interventions classified astreating or limiting symptoms of the disease. For example, if a user isdiabetic and is determined to be in a community with an increasingprevalence and transmission rate for COVID-19, the computer system 110can communicate with the user's mobile device to instruct caution anddisease exposure avoidance, and may also initiate or intensifyinterventions to improve respiratory fitness and monitoring to help theuser better prepare for or overcome COVID-19 symptoms.

FIG. 20 is a flow diagram showing an example of a process 2000 ofcollecting data and utilizing the data to improve disease management forindividuals and communities. The process 2000 can be performed by one ormore computers, such as the computer system 110. In general, theoperations of the process 2000 can be performed by one or more servers,one or more client devices, or a combination of them. In addition to theoperations illustrated, the process 2000 can optionally include otheroperations discussed with respect to FIG. 11. In the example, thedisease mentioned can be COVID-19, or it can be another infectiousdisease.

The process 2000 can be used in an ongoing, iterative way to respond tochanging conditions in a community and tailor the data collection in thecommunity. This allows the computer system 110 to customize the level ofinteraction and disease monitoring performed for individuals in acommunity (e.g., through capture of sensor data and though capture ofuser responses to surveys and other prompts). The computer system 110can collect data about individuals and/or communities. When certaincharacteristics of the collected data are detected (e.g., certainvalues, patterns, trends, etc.), this triggers changes in the datacollection process used. The changes can include, for example, expandingdata collection to a larger set of individuals or communities, changingthe types of data collected or manner of collecting data, initiatingspecific, focused questions or interactions determined to be relevant todetected conditions in a community, and so on. This allows the computersystem 110 to adapt data collection procedures quickly to changes in acommunity, such as changes in disease prevalence or disease transmissionpatterns or to changes in user behavior patterns.

As an example, the computer system 110 may initially cause certainmonitoring actions to be performed for a first group of users. Forexample, EMAs or survey may ask questions such as “Is the store you arein busy today?,” or “Has anyone you know been diagnosed with COVID-19?,”or “have you had a headache in the last 24 hours?” These questions orother interactions can be designed to gauge certain disease measures orrisk factors for an individual or the individual's community. Thecomputer system 110 then evaluates responses with respect to a set ofcriteria and, if the criteria is met, the computer system 110 expands orotherwise adapts the data collection process. For example, if one ormore users in the first group indicate exposure or high risk of COVID-19infection, the computer system 110 may naturally respond with follow-upquestions to find out more from those users. In addition, or as analternative, the computer system 110 may adjust data collection forother users in response, such as by initiating questions to users in asecond group, who may be unrelated and have had not any contact with theusers in the first group. For example, the computer system 110 caninitiate more widespread surveys throughout a community, where thecontent is selected or adjusted based on the conditions or criteria thatthe responses for the first set of users met.

As a result, from the results of data collection for one or more usersin a community, the computer system 110 can adapt the data collectionfor other users in the community. Various types of data can trigger theadaptation or can have their collection processes adapted, such asbehavior data (e.g., location tracking data, activity data, movementdata, food intake, social activity monitoring, etc.), physiological data(e.g., blood pressure, heart rate, glucose levels, oxygen saturationsuch as SpO2, breathing rate, lung capacity or VO2 max, bodytemperature, etc.), mood and mental health data, health records (e.g.,EMR/EHR), and so on. This data can be manually entered data (not onlydata from device-driven collection), data from medical records orhistorical records, and so on. Data collection for a user can involvethird-party devices, which may or may not be networked orcommunicatively connected with the user's device, e.g., smart phone,medical treatment device, etc.

The process 2000 optionally includes collecting data about individualsand/or communities (step 2002). This data collection can be done asdescribed with respect to FIG. 19 (see steps 1092, 1094), FIG. 11, andothers discussed above. Nevertheless, this step is optional, as thecomputer system 110 may access or evaluate data from a repository thathas been previously collected or compiled (e.g., by another system).

As noted above, data collection can include collecting user input dataprovided by one or more individuals in a community to their respectiveuser devices, e.g., mobile devices. These inputs can be prompted byinteractions initiated by software applications on the user devices orthrough communications from the computer system 110 over a network. Thecomputer system 110 can instruct a software application running on userdevices to provide certain questions or prompts to users, where thequestions or prompts may be standardized (e.g., common or the same formultiple members of a community) or customized for individual usersaccording to their respective contexts and needs. Of course datacollection can occur through other techniques, such as passivecollection of sensor data, obtaining data from third-party computersystems (e.g., to collect health records), and so on.

The process 2000 includes accessing trigger data indicating datacollection triggers (step 2004). The trigger data can specify triggersdesigned or designated for monitoring of a specific disease, such asCOVID-19. Each data collection trigger can specify one or more criteriaused to determine whether the trigger is present or not. The criteriacan be any of various types, such as a value of a measurement satisfyinga threshold or being in a range, a particular response or combination ofresponses being received, a pattern or trend being present in collecteddata for one or more individuals, the presence of a biomarker or digitalmarker for health status, a change in behavior over time, etc. Thecriteria may be based on physiological measures, behavioral measures,user-reported symptoms, collected sensor data, health records, and/orother types of data or combinations of multiple types of data. Dependingon the trigger, the corresponding criteria may be based on collecteddata for a single individual in a community, based on collected data formultiple individuals in the community, or for community-level statisticsor data.

Each data collection trigger can specify a type of data to be collectedwhen the corresponding one or more criteria are satisfied. In someimplementations, the data collection trigger is focused on datacollection for a community, and not particular for the user whosecollected data may have caused the data collection trigger to occur. Asa result, the type of data associated may be focused on assessing theconditions and characteristics present in the community generally, notnecessarily the situation of any specific user. In other words, the datacollection trigger can indicate a risk factor or situation of one user,and the type of data to be collected can be used to assess the overallconditions of the community or to prepare to more quickly detect newdisease-related conditions that are now more likely to develop in thecommunity.

Examples of trigger data are shown in FIG. 21, which shows a table 2100in which different rows represent different data collection triggers.Column 2010 indicates criteria for the different triggers. Eachindicates a condition, and the computer system 110 evaluates thecollected data to determine if any of the conditions are present. Column2020 shows types of data to be measured when the corresponding criteriaare met. In the example, these are general categories, but inimplementations may be more specific. For example, for the firsttrigger, if the community infection rate is greater than a threshold,the trigger data indicates that information about disease symptomsshould be collected. Optionally, more specific data such as data aboutspecific symptoms (e.g., headache, gastrointestinal discomfort,coughing, etc.) and/or physiological data could be specified. Thecriteria can be based on conditions measured for a community as a whole,a sub-group within the community, or for individuals in the community.

The conditions can be based on physiological measures, behavior,indicators of mental health, disease status (e.g., community diseasemeasures or individual disease status), and so on. In some cases, thecriteria for a data collection trigger can be based at least in part onphysiological measurements. For example, digital thermometers may beused at locations in a community or may be distributed to a random groupwithin the community. Body temperature changes can signal a need forincreased monitoring, such as asking about further symptoms (e.g.,breathing difficulty). Physiological data for individuals can alsotrigger data collection for other information from the individuals aswell as others, such as their family members or household, others attheir place of employment, groups at high risk for COVID-19 exposure ordisease severity, etc.

Other columns in the table 2100 show other optional types of data thatcan be used to collect information. Column 2030 indicates content or atool to be used in collecting the data, such as a specific question,survey, sensor, device, etc. Column 2040 indicates a data collectionmode to be used to collect the data, such as through user input, sensingduring a user activity (e.g., an exercise, game, or other activity)prompted by the system, or through collection of sensor data. Column2050 indicates a set of individuals for whom the detected trigger causesdata collection to be performed.

Other techniques for specifying trigger data can be used, for example, adecision tree can specify different actions (e.g., data collectionactions) for the community based on different data or factors determinedto be present. The technique shown in FIG. 4 for determining actions forindividuals can be applied for communities and groups of individualswithin communities, to specify different data collection actions toperform.

Referring again to FIG. 20, the process 2000 includes detecting one ofthe data collection triggers indicated by the trigger data (step 2006).The computer system 110 can detect that a particular data collectiontrigger occurs by determining that the one or more criteria for theparticular data collection trigger are satisfied. For example, thecomputer system compares characteristics of collected data withpredetermined criteria for the data collection triggers and determineswhen the criteria for a trigger is satisfied. The detection can be donein substantially real time, such as in response to receiving collecteddata. Additionally or alternatively, the analysis to detect triggers canbe done periodically, e.g., hourly, daily, etc.

The process 2000 includes determining content configured to prompt userinput for a type of information associated with the detected datacollection trigger (step 2008). This can involve selecting a specificquestion, user form, survey, or other interactive content to obtain auser input providing the needed data. While this example emphasizes userinput as the mode of data collection, the computer system 110 mayadditionally or alternatively determine to initiate data collectionthrough other techniques, such as through initiating or adjustingcollection of sensor data, through requesting or retrieving medicalrecords, and so on.

The trigger data may associate a specific existing content (e.g., anEMA, a form, a question, a survey, etc.) with the trigger, and the samecontent may be provided to each recipient. As another example, thecomputer system 110 may use the indication of the type of data to becollected (e.g., resting heart rate) to generate or customize tailordifferent interactions for different recipients. For example, one userwhose device can measure resting heart rate (or is wirelessly connectedto a device that can) can be instructed to measure and report this data.Another user whose device does not have heart rate monitoring capabilitycan be provided a question asking the user to enter the user's restingheart rate. As another example, for generating content to prompt forinformation about breathing symptoms one option is to provide everymember of the community the same question, e.g., “Have you experiencedany trouble breathing today?” Another option is to generate differentquestions to acquire the needed information, customized for theinformation known about users, with different users respectivelyreceiving questions such as “You had trouble breathing yesterday. Was itany better today?,” “Your respiration rate has increased. Are you havingtrouble breathing?,” and so on.

The process 2000 includes selecting a set of individuals associated witha community, so that the individuals in the set can receive the contentthat prompts user input (step 2010). The computer system 110 can store,for each community, data indicating a set of individuals associated withthe community. This can be, for example, a set of people in thecommunity who have enrolled or registered to participate in a communityhealth program, a research study, or other initiative. These individualsin the community can each have a user device with the softwareapplication that enables data collection and reporting. In response todetecting the data collection trigger, the computer system 110 selects agroup of the individuals associated with the community. The set ofindividuals may be all members of the community or a subset having fewerthan all of the members of the community. For example, the trigger datamay specify certain selection criteria that are used to determine thesubset of people for which the additional data will be collected. Asanother example, the computer system 110 may determine the set ofindividuals based on the characteristics or needs of the community. Forexample, the computer system 110 can identify risk factors for diseasespread or gaps in information about the community, and can select a setof individuals who can provide information to assess the risk factors orfill the gaps in information about the community.

The process 2000 includes communicating with devices of the selected setof individuals to cause presentation of the selected content thatprompts user input (step 2012). For example, the computer system 110 cansend a message or configuration data to the user device of eachindividual in the selected set of individuals. The message can instructthe user devices to perform the needed data collection, e.g., to causeeach user device to present the content (e.g., a survey) that promptsuser input of the needed type. The computer system 110 can store dataindicating the user devices or user accounts for different users and usethis to target content delivery to the right individuals. The contentcan be pushed to user devices, such as through a notification thatappears and alerts the user. In other cases, the content can be providedthrough an application interface or web page that a user sees when theuser opens the application or web page.

The process 2000 includes receiving user responses to the prompts foruser input (step 2014). The computer system 110 receives and stores theuser responses. These responses can be, for example, interactions withbuttons, sliders, checkboxes, and other interactive user interfaceelements. Other responses can be entered text, numbers, or other dataprovided. When data collection actions involve changes to the collectionand reporting of sensor data, the computer system 110 can furtherreceive collected data generated through those functions.

After receiving the user responses, the computer system 110 can evaluatethe received responses to determine whether any additional datacollection triggers are detected. This is represented in FIG. 20 by thearrow 2015 showing that user responses can lead to detection ofadditional data collection triggers, which can then further customizethe disease monitoring processes performed for a community.

The process 2000 optionally includes adjusting disease predictions anddisease management actions for individuals and communities (step 2016).This can include selecting or adjusting monitoring, detection,prevention, testing, and/or treatment for the disease for an individual,as discussed above. The information can also be used to perform aniteration of the process 1900 of FIG. 19, to generate updatedpredictions for a community based on the collected data. As discussedfor FIGS. 1-5, 11-13B, and other areas above, the data collected forindividuals and communities can prompt a variety of interventions,including changes to data collection techniques, selecting and providingdisease testing kits, selecting and recommending disease preventionmeasures, selecting and providing medical treatment or monitoringdevices, selecting and providing vaccines, selecting or and providingtreatment, and so on.

In some implementations, the process 2000 can be used to initiatedisease management actions other than data collection. In the samemanner that the table 2100 shows data collection triggers andcorresponding data collection actions, the computer system 110 can storeand use triggers for corresponding actions for other purposes, such asdisease management actions, providing treatment, providing vaccines,providing testing kits and so on.

FIG. 22 is a flow diagram showing an example of a process 2200 ofdetermining disease exposure using location tags and geofenceinformation. The process 2200 can be performed by one or more computers,such as the computer system 110. In general, the operations of theprocess 2200 can be performed by one or more servers, one or more clientdevices, or a combination of them. In addition to the operationsillustrated, the process 2200 can optionally include other operationsdiscussed with respect to FIGS. 14-18. In the example, the diseasementioned can be COVID-19, or it can be another infectious disease.

In some implementations, rather than relying only on device-to-deviceinformation passing or human contact tracing staff, devices can check inwith their locations to a server system. The locations that infected orlikely to be infected people visit are tagged in the map data of theserver. A series of geofence limits then track which other users enterthe geofenced, tagged areas. The amount of time spent in the areas, aswell as the length of time since the infected person visited are used toweight the exposure risk for each geofence entry event. The exposureestimates then drive targeted interactions with the userrecommendations, sending testing kits, etc. The data is also aggregatedacross different users to generate an estimate of community-levelexposure risks.

As discussed above, the computer system 110 can be used to improvelocation tracking and contact tracing for disease exposure estimation.Some technology-based contact tracing approaches rely on user devices totransmit and receive wireless messages to each other, so that receptionof a message with a device's identifier indicates co-location with thatdevice. When a user's phone detects a message from a phone of someonedetermined to have a case of COVID-19 (e.g., both devices are at thesame location at the same time), the user can be notified of theexposure. However, COVID-19 transmission does not require being in thesame place at the same time, because airborne infectious particles canremain viable for hours (e.g., sometimes 4 hours, 8 hours, 12 hours,etc. depending on the environment), and infectious particles can alsoremain on and be transmitted on surfaces for significant periods oftime. In addition, a person may be contagious days before receiving atest and confirmation of a case of COVID-19, and exposures that occurredbefore the test need to be tracked and accounted for.

The present technology can account for these factors by, among otherfeatures, detecting exposure that occurs within a window of time thatincludes an infected person's presence at a location and a subsequentperiod of time afterward (e.g., 4 hours in a well-ventilated area or 12hours in a poorly ventilated area, or with other times to account forperiods of surface-contact transmission). In addition, the system 110can account for exposures that occur before a positive test of anindividual. The system 110 can track and store information about users'movements, such as through location tags representing visits at specifictimes and locations. For users who have tested positive for COVID-19,the location tags corresponding to their visits are marked or labeled toindicate this, for example, with a label indicating the health status ofthe visitor (e.g., positive case of COVID-19), an indication that actualexposure occurred, and/or with a disease transmission score indicating ahigh level of transmission risk to show actual exposure occurred.

Even if a user has not yet tested positive for COVID-19, the locationtags can be used to represent the exposure risk. One way is by markingor associating location tags with the predicted infection likelihoodgenerated using predictive models 230 (see FIG. 3). This allows thecomputer system 110 to use data about physiological attributes andbehavior of a user (e.g., captured through user responses to questions,automatic capture of sensor data, etc.) to predict when a user is likelyinfected before a test result is available, and so provide earlierwarnings and notifications to other users who visit the same places atthe same or similar times.

In addition, once a positive test result is obtained for a user, thecomputer system 110 can update the data for location tags of the user toreflect this new information. For example, if a user tests positive,location tags for subsequent visits will be marked to show that theycause COVID-19 exposure. In addition, the location tags representingprevious visits for some period of time (e.g., 5-10 days, which may varydepending on the stage of disease when tested) can all have theirinformation updated to reflect the fact that those visits are now likelyto have caused COVID-19 exposure. The disease transmission scores forthe previous, pre-testing visits can be similarly updated to show ahigher risk of transmission. Consequently, based on the updates to thedisease transmission scores, the disease exposure scores for individualsthat entered the geofences of those location tags can also be updated toreflect the new information that actual exposure occurred. This allowsthe computer system 110 to track and use exposure events that occurbefore diagnosis or testing of a user, which is often not possible to doaccurately and efficiently with traditional contact tracing techniques.

The process 2200 can be performed to create a personalized diseaseexposure risk score for an individual, based on the locations that theindividual visits over a period of time. The computer system 110 cantrack locations that individuals visit, using location monitoring datafrom a GPS receiver or through other means. The computer system 110 canadditionally or alternatively track location using user-reported data,for example, provided in response to prompts or surveys. The locationsthat user visit are tracked, and when a user is confirmed to haveCOVID-19 or is classified as likely to have COVID-19 (e.g., such as forhaving an infection prediction likelihood above a threshold), thelocations that user has visited are assigned a disease transmissionscore indicating the potential for that visit to transmit COVID-19. Thevisit by an infected or likely infected person is considered to presenta level of exposure risk (quantified by the disease transmission score)to others that subsequently visit the location within some maximumperiod of time (e.g., the next 12 hours after the infected person leavesthe location).

Having marked locations that present exposure risk, the computer system110 can aggregate information about a user's visits over a period oftime (e.g., a day, a week, etc.) to estimate the level of exposure tothe disease or risk of contracting the disease based on past visits.More visits to areas with risk tags, and longer visits at thoselocations, result in greater levels of individual risk being estimated.One of the advantages of the system is that it can capture the potentialfor disease spread even when individuals are not in the same location atthe same time. Even if a second user arrives after the first user hasleft, the system can still assess the risk of that visit. Whendetermining the exposure score for a user, the transmission scores forlocations can be weighted or decreased according to the amount of timethat has passed since the infected or likely infected person left. If anfirst user, who has an active case of COVID-19, visited a store at 1:00pm, and a second person arrived at 2:00 pm, there is still a risk thatinfectious COV-SARS-2 particles from the first person are still presentin the air or on surfaces when the second person arrives. Nevertheless,the risk diminishes over time, and the risk to the second user would bemuch less at 8:00 pm. Thus, the aggregation of data about locations thatthe second user visits can be adjusted to account for factors such astime elapsed, nature of the activities being performed at the location,a degree of overlap between paths of the two users along the location,duration that the two users respectively spent at the location, and soon.

After an exposure score for a user is calculated, the computer system110 may update the exposure score based on later-received information.For example, the computer system 110 may determine a low exposure scorefor a particular user for her activities on Monday. On Wednesday,however, new COVID-19 test results may be received, showing that one ormore other users who on Monday visited a same location as the particularuser have tested positive for COVID-19 and thus were likely infectedduring the Monday visits. As a result, the computer system 110 updatesthe location tags for those visits on Monday with increased diseasetransmission scores. The computer system 110 also updates the diseaseexposure score for the particular user for Monday to show that exposureto COVID-19 was higher than previously calculated now that the visits ofthe other users have been re-classified as visits causing exposure orhaving high likelihood of exposure.

The process 2200 includes receiving location data for individuals (step2202). This can include automatic location sensing of the location of auser device of a user, e.g., a smart phone, wearable device, etc. Forexample, location data can be tracked using GPS data, and/or throughreception of messages from wireless beacons, cellular towers, Wi-Fiaccess points, or other transmitters having known locations andidentifiers. In addition or as an alternative, the system 100 can prompta user for input to indicate locations the user has visited, and userresponses can indicate locations of users at different times.

The process 2200 includes generating location tags based on the locationdata (step 2204). The location tags can be records or data entries thatspecify visits of the user devices to different locations indicated bythe location tracking data. Location tags can be generated or recordedin response to determining that location tracking data meets certaincriteria or has a predetermined set of characteristics. For example, atleast a minimum duration of time at a location may be required to definea location tag, e.g., 1 minute, 5 minutes, etc. As another example,movement data can be assessed to determine whether the user's behaviorrepresents the kind that may cause exposure. For example, a usertraveling in a car would have a motion pattern that can be excluded, sothat stopping at a stoplight does not create a location tag or exposureevent for the corner store where the car stopped temporarily. In someimplementations, visits that meet the criteria for timing and locationor movement patterns are defined for all users. In otherimplementations, location tags are selectively generated for visits onlyin response to determining that there is a significant likelihood thatthe visiting user could transmit the disease (e.g., the user testedpositive for the disease, whether before or after the visit; theinfection likelihood score for the user at the time of the visit exceedsa threshold; etc.)

Each of the location tags can have corresponding data indicating alocation of a visit, a time of the visit, and a user device or usercorresponding to the visit. More specific information can be included,such as the time of entry, the time of exit, the nature of the visit, apath of movement during the visit (e.g., through store aisles or arounda park), and so on.

As discussed above, location tags can be associated with various othertypes of information, such as characteristics of the location, locationtype, current conditions at the location, disease prevention measures inplace at the location, compliance with disease prevention measures(e.g., by asking one or more users at the location through an EMA orother prompt), and so on. In addition, information about the user whosevisit is represented by a location tag (e.g., the user whose presencerepresents a potential COVID-19 exposure to others at the location) canalso be stored in or associated with the location tag, e.g., a useridentifier (e.g., an anonymized identifier), a health status withrespect to the disease (e.g., active case; recovered case; vaccinated;probable case based on symptoms or machine learning predictions; neithervaccinated, recovered, probable, nor confirmed case; etc.), whether theuser used disease prevention measures and which ones, and so on.

The system 110 or a user's mobile device can initiate interactions witha user to collect information about the visit. This can include askingabout the environment or conditions present, the number of other peoplepresent, the user's activity or task being performed, etc. The system110 can ask whether the user is using (or previously used) a mask orother preventative measure during a visit.

Public transportation vehicles or routes can be considered to belocations and can be tagged based on use by individuals. For example, ifa user gets on a bus, the specific bus may be tagged based on the user'spresence. Similarly, if the user gets on a subway, the subway car ortrain may be tagged. In this case, the location tag specifies not afixed location but an identifier for the vehicle and potentially theroute the vehicle travels. In cases where it is not possible to tag thespecific vehicle, the route of the vehicle may be tagged.

The process 2200 includes assigning geofences for the location tags(step 2206). The geofence represents a boundary or defined region towhich the location tag applies. Each geofence encompasses or defines ageofenced area corresponding to the location tag. The geofenced arearepresents an area affected by the visit of the user whose presence ledto the creation of the location tag. For example, the geofenced area canbe an area in which the disease may be transmitted based on the visit ofan infected persion, e.g., an area in which the likelihood of infectionfor a concurrently-visiting or later-visiting user is increased if theuser enters the geofenced area during a period of time (e.g., a windowcomprising the duration of the visit of an infected or likely infectedindividual plus an additional period of virus viability, such as 12hours).

In some implementations, a separate geofence can be assigned fordifferent location tags. For example, the size and shape of a geofencecan be based on the range and path of movement of a user at thelocation. Thus, if a person visited a large store and only entered onesection of the store, the geofence may be restricted to covering thesection actually traversed during the visit. Nevertheless, in otherimplementations, the geofenced area can be a standard area for thelocation, e.g., a single geofenced area representing the entire storewhen the user visits any part of the store. In some implementations, thegeofence may be specified by an address, and separate map data can beused to look up the boundaries that correspond to the address. Asanother example, a geofence may be specified by GPS coordinates and aradius or an address and a radius. As another example, a geofence may bespecified using polygons or other shapes to specify the boundaries ofthe geofenced area.

The process 2200 includes assigning disease transmission scorescorresponding to location tags (step 2208). Each location tag for avisit can be assigned one or more disease transmission scores indicativeof the level or risk that the visit represents in terms of potentiallyexposing others who concurrently or subsequently visited the location.In other words, a disease transmission score can indicate a potentialfor disease transmission that occurred as a result of a user's visit toa location. The disease transmission scores can be based on dataindicating a disease status of the user whose visit is represented bythe location tag. The score can be based on actual confirmed diagnosis(such as a positive test based on a nasal swab testing kit), a probableinfection such as due to likely exposure and symptoms indicating highinfection likelihood, or a prediction of infection from a machinelearning model.

In some implementations, the disease transmission score can be as simpleas an indication (e.g., yes or no) whether the user had (or hassubsequently received) a diagnosis or positive test indicating aconfirmed case of COVID-19. In other implementations, the diseasetransmission score can be more nuanced, for example, having differentvalues along a scale to indicate different levels of potential fordisease transmission, e.g., a score of 10 for a confirmed case, a scoreof 7 for a probable case, a score of 3 for a relatively low infectionprediction score, etc. The disease transmission score can vary accordingto collected data for a user, e.g., the current and recent signs andsymptoms of the disease detected or reported for the user; based ondifferent levels of infection likelihood prediction (e.g., score of 7for a 70% likelihood prediction, a score of 2 for a 20% likelihoodprediction, etc.); and so on.

The disease transmission scores can be generated or adjusted based onvarious factors other than the disease status of the user, to reflecthow conditions present during the visit may have affected the ability ofinfectious particles to spread, persist, and infect others. For example,a value determined from the disease status of the user may be increasedor reduced by additional terms that represent at least one of theactivity of the user, characteristics of the user, whether the user useddisease prevention measures, the characteristics of the location, theduration of the visit, and so on. The disease transmission scores can begenerated or adjusted based on the conditions at the location and thebehavior of the user and others. For example, if face masks are worn andpeople are following social distancing rules, the disease transmissionscore can be reduced, to reflect the lower likelihood or risk ofinfection under those conditions. Similarly, whether the infected usermaking the visit wore a face mask or not can affect the likelihood oftransmission and thus the disease transmission score. As anotherexample, the disease transmission score can vary according to the lengthof a user's visit (e.g., and the amount of time the user had to spreadinfectious particles), the movement of the user or activity or task ofthe user at the location, the type of location, the characteristics ofthe location, and so on.

For example, an infected person that visited a store can have a basedisease transmission score of 10 to represent the exposure from aninfected user. This base score can be modified based on one or morefactors to more accurately reflect the risk the visit presents. Forexample, the base score may be: decreased because the user reportedwearing a mask during the visit (e.g., an adjustment of −2); decreasedbased on social distancing being required by the store or city (e.g., anadjustment of −2); increased because of a large area covered by the userduring the visit (e.g., +1); increased based on the duration of thevisit being 45 minutes (e.g., +3, one point for each 15 minute period inthe visit); increased based on the enclosed nature of the store andlimited ventilation (e.g., +2); etc. As a result, the diseasetransmission score can be a combination of these factors, resulting in ascore of 12 for the location tag.

Other techniques for generating disease transmission scores can be used.For example, a predictive model (e.g., a machine learning model, astatistical model, a rule-based model, etc.) can be trained based ontraining data showing disease transmission outcomes under differentconditions (e.g., different combinations of location types, locationcharacteristics, visit durations, disease prevention measures used,etc.). Based on the relationships demonstrated by the training data, themodel can be generated or trained to determine the significance ofdifferent factors (e.g., the level of impact of differentcharacteristics or inputs on disease spread), and can learn to provideoutputs that quantify the potential for disease transmission. Todetermine the disease transmission score for a location tag, input datadescribing the collected data describing the visit (e.g., duration,location characteristics, preventative measures used, etc.) can beprovided to the model, and the model can output a disease transmissionscore reflecting the overall risk of transmission for the visit. Diseasetransmission scores can be determined in other ways, such as with afunction, equation, look-up table, etc. configured to assign appropriaterisk levels as determined from training data.

The process 2200 includes determining disease exposure scores forindividuals based on their entry to the geofenced areas (step 2020). Thedisease exposure score can be a measure of how likely and/or howintensely a person was exposed to COVID-19 or another disease. Using thelocation tracking data, the computer system 110 can determine, for eachuser participating, which geofenced areas the user entered during aparticular time period (e.g., the last hour, the previous day, theprevious week, etc.). The computer system 110 can then combine oraggregate the exposure risks for the different geofenced areas entered(e.g., transmission risk information for the applicable location tags)to determine a disease exposure score for the user for the particulartime period. In this manner, the computer system 110 can regularlygenerate a disease exposure measure (e.g., every hour, every day, etc.)for each user. Disease exposure measures can also be generated in realtime, responsive to new risks detected, such as in response to a newvisit to a location by a user. In some implementations, a user can beconsidered to enter a geofenced area in a manner that receives exposureonly when predetermined criteria about the visit to the area aresatisfied, such as remaining in the area for at least a minimum amountof time (e.g., 1 minute, 5 minutes, etc.), the movement pattern isconsistent with activity that would cause exposure (e.g., walking orbeing present in person, not merely driving through in a vehicle), etc.

The computer system 110 can compare the disease exposure measure for auser to reference data to determine notifications, recommendations, andother interventions to provide. For example, the computer system 110 cancompare the disease exposure score to thresholds to determine a riskclassification for the user's exposure level, and whether diseasetesting, disease treatment, a medical appointment, additionalpreventative measures, or other interventions should be provided. Asanother example, the computer system 110 can compare the diseaseexposure score to prior scores for the user to determine whether theexposure is increasing or decreasing, and inform the user accordingly.

In essence, the computer system 110 evaluates the overall, cumulativeeffect of all of the tracked visits for the user during a time period.The process can take into account how many times, and for whatdurations, the user's tracked visits overlapped in space with thegeofenced area representing current or previous visits by any infectedperson. The process can also take into account the temporal closeness ofa user's visits to prior visits of infected or likely infectedindividuals, such as by weighting or scaling the disease transmissionscores according to how much time had elapsed between the user's visitand the prior visits of infected or likely infected individual.

One way to generate the disease exposure score is for the computersystem 110 to aggregate the disease transmission scores for the locationtags for which the user entered the corresponding geofenced area in theparticular time period corresponding to the disease exposure score. Forexample, based on the location tracking data for a user, a user isdetermined to have entered at least one of the geofenced areascorresponding to the location tags representing potentialdisease-transmitting visits by other users. A disease exposure score isdetermined by aggregating disease transmission scores for differentlocation tags for which the user device is determined to have enteredthe corresponding geofenced area. The aggregation can be done using anyof various techniques, such as a summation, a weighted average, takingthe maximum value of the disease transmission scores, providing datacharacterizing the visits and disease transmission scores to machinelearning model for classification or regression, and so on.

When determining the disease exposure score for a user, the diseasetransmission score for a location can be adjusted or modified to reflectthe situation and characteristics of the user's visit to the geofencedarea. The disease transmission score associated with a location tagreflects only one side of the person-to-person transmission, thetransmission potential by the transmitting user and environmentalfactors that affect the likelihood and extent of infectious particlesspreading. This can be further modified or customized for the userpotentially receiving exposure to the disease, since the receivinguser's actions may cause the user to be exposed to a different degree(e.g., depending on factors such as whether preventive measures likemasks are used, the duration of the visit to the exposed area, etc.)

Thus, in the aggregation to determine an exposure level received by auser, the level of exposure or exposure risk for each entered geofencedarea can be based on various factors regarding the user, the visit, thelocation, and so on. A significant factor in the adjustment is theamount of time elapsed between (i) the departure of the user whose visitcaused the location tag to be created and (ii) the arrival of the userwhose exposure score is being determined. This difference is referred toas a “visit time difference” below. The visit time difference can beused to provide a gradual or incremental decrease in transmissionpotential in the period after an infected person leaves. For example,the effective exposure score for a visit can be calculated by retrievingthe disease transmission score for the location tag and then scalingthat value downward by an amount determined based on the visit timedifference and decreasing function (e.g., decreasing linearly,quadratically, exponentially, according to a statistically determined ormachine-learning determined pattern, etc.). For example, the adjustmentcan be a scaling factor determined based on the visit time difference,where an actual overlap in time of visits (e.g., a visit time differenceof zero) can have a scaling factor of 1.0 (e.g., causing no adjustmentto the full disease transmission score), a visit time difference ofbetween one and two hours results in a scaling factor of 0.8, a visittime difference of between 6 and 8 hours results in a scaling factor of0.4, and so on, until a maximum visit time difference (e.g., 12 hours ormore) for which the scaling factor is zero to indicate that the visitwould not contribute to disease exposure. Similarly, an amount of timetwo visits actually overlapped can be an additional adjustment factor,e.g., to increase the level of exposure a user received. Other factorsthat can adjust the transmission scores for the aggregation include theduration of time the user spent in the geofenced area, diseaseprevention measures of the user (e.g., whether the user wore a mask),the environment at and characteristics of the location, behavior oractivity of the user at the location, etc.

As discussed above, the computer system 110 customizes the diseasetransmission score to obtain an exposure score for each visit, accordingto the nature of the visit and the user's corresponding potential toreceive exposure. The computer system 110 then aggregates these exposurescores (e.g., adjusted disease transmission scores) for different visitsto determine the overall disease exposure score for a time period. Asnoted above, the exposure scores for different visits can be summed orotherwise combined to show a measure of cumulative exposure. As anadditional or alternative measure, the maximum value (e.g., highestexposure level) of these exposure measure can be used, quantifying aseverity of the highest-exposure event for the time period. As anotherexample, a machine learning model, trained based on examples ofdifferent exposure events and corresponding infection outcomes, canprocess information about the visits and/or exposure scores and canprovide an aggregate measure of exposure to be used as the diseaseexposure measure.

Another approach to determining the disease exposure score is to scaleeach disease transmission score (e.g., with or without adjustments) bythe amount of time the user spent at the location where exposureoccurred. Thus, the disease exposure score can be represent an amount oftime accumulated at different exposure levels. For example, a user mayhave in a day three visits to areas having transmission scores of 10,15, and 20, respectively. The visits may have durations of 15 minutes,30 minutes, and 15 minutes, respectively. With a scaling factor of 1.0for one hour spent at a location, the cumulative exposure level can be:0.25*10+0.5*15+0.25*20=15.

As discussed above for FIGS. 14-16D, additional location tags can bedefined for other risks, such as the population levels or traffic levelsat different areas and the types of locations (e.g., reflecting the waylocations of that type are used). The computer system 110 canadditionally or alternatively generate disease exposure scores for auser based on these tags. For example, the disease exposure score canaggregate exposure risk from any or all of visit-based location tagsrepresenting the presence of specific individuals at locations,population-based location tags indicating the volume and characteristicsof people at a location (e.g., percentage of people vaccinated), andlocation-based tags representing the types of locations present. Diseasetransmission scores for the population-based tags and thelocation-type-based tags can be scaled based on visit duration,preventative measures used, and other factors as discussed above, andcan be aggregated together for different visits as discussed above forthe visit-based tags.

The exposure scores for a user can be modified based on informationabout the user, such as the disease status of the user. For example, insome implementations, the exposure score for a user may be modified ifthe user has previously been vaccinated or recovered from COVID-19, andso has a lower risk of contracting the disease.

The process 2200 includes selecting and communicating, by the one ormore computers, a recommended disease management action for individualsand/or communities (step 2212). The computer system 110 can storemapping data that associates different conditions with correspondingdisease management actions. For example, different combinations of usercharacteristics and different disease exposure score levels may havedifferent corresponding actions. The condition of a user over 60 yearsold who has an exposure score of at least 10 may correspond to arecommendation to receive a vaccine as soon as possible. The conditionof any user having an exposure score over 20 may be encouraged to take atest for COVID-19. The condition of a user having an exposure score over5 for multiple consecutive days can correspond to a recommendation towear a mask. The condition of a user that has an exposure score of over30, who has one or more disease symptoms, and does not have acontraindication for a particular drug used to treat early-stageCOVID-19 infection can correspond to recommending use of the drug,either to the user's doctor or directly to the user. Many other types ofconditions and corresponding recommended disease management actions canbe used.

The conditions and the corresponding actions can be determined based onstatistical analysis of the effectiveness of disease management actionsfor the different exposure levels. In some implementations, therecommendations can be determined based on output of a machine learningmodel. The model can be trained based on examples of exposure scores,disease management actions applied, and corresponding disease outcomes.The training can teach the model to predict the effectiveness of or needfor different disease management actions, given the exposure scores andother data describing a user's situation (e.g., user characteristics,collected data, community disease measures, etc.). Thus, the outputprovided by the model upon processing the exposure scores for a user canbe scores indicating the respective applicability (e.g., level of needor level of predicted effectiveness) for the level of exposure that hasoccurred. In some cases, a model can be configured to receive andprocess feature values indicating detailed information about exposureevents (e.g., duration of exposure, location type, preventative measuresused, etc.) in addition to or instead of the disease exposure scores.

The computer system 110 can provide various outputs based on theexposure level analysis (e.g., exposure risk analysis). Individuals canbe provided their disease exposure scores, exposure risk levels,classifications of the magnitude or types of exposure, or otherinformation to characterize the exposure that has occurred. In addition,the computer system 110 can notify individuals of specific events thatrepresent high exposure levels or high exposure risks, either as theyare detected (e.g., in the context while the exposure is occurring) orretrospectively. As discussed above, the disease exposure scores canalso trigger further data collection for individuals through sensor datacollection, prompts or surveys, and so on.

The computer system 110 can also provide information about exposure atthe community level, as well as recommendations for the community. Therecommendations may be provided to community leaders, communitybusinesses, and public health agencies, or in some implementationsdirectly to community members. As with recommendations for individuals,mapping data can specify different predetermined conditions andcorresponding disease management actions to recommend. For example, ifthe average exposure score for users in the community exceeds 5, thensocial distancing can be recommended. As another example, if at least aminimum number of exposure scores exceed 10, then a general alert can berecommended for the community. If exposure levels at a specific locationor location type exceed a threshold, then disease prevention measurescan be recommended (e.g., mask usage, social distancing, closing thelocation, etc.).

Community-level recommendations can include changing policies fordisease prevention, such as enacting or removing specific diseaseprevention measures. Depending on the exposure levels and communitycharacteristics, these may be provided for the community as a whole, forspecific location types, for specific locations, etc.

The exposure levels throughout the community can be used to determinewhen disease prevention measures can be relaxed or removed. For example,based on a pattern of decreasing exposure levels or exposure levelsbelow a threshold for at least a minimum length of time, the computersystem 110 can recommend that preventative measures are no longerneeded. For example, the computer system 110 can indicate periodically(e.g., daily) which preventative measures are appropriate for thecurrent and recent pattern of exposure in the community, and as theexposure scores decrease, the set recommended can be changed to showthat previously recommended measures (e.g., limit to 25% occupancy) maybe lifted (e.g., limit to 50% occupancy or no need to limit occupancy).The recommended lifting of preventative measures can be done in a waythat gradually or incrementally removes these measures.

The location tag information can be aggregated to determine the areas ina community that represent hotspots of disease transmission. Beyondaggregating the number of tags, the disease transmission scores for tagsor the actual exposure scores for different exposure events can be usedto show where the greatest level of exposure is occurring.Recommendations to modify use of hotspot locations, e.g., to limitoccupancy, to enforce social distancing, to close the location, etc. canthen be provided, along with data identifying the locations for whichthe measures are recommended.

Some community-level recommendations may include recommendations ofvaccination programs, such as for certain sets of individuals or certainareas in the community, e.g. sets of individuals the computer system 110identifies as having the highest exposure or risk levels; individualsthat reside in, work at, or visit areas of highest disease transmission;areas determined to have at least a certain number of or percentage ofindividuals with high susceptibility to the disease (e.g., from usersusceptibility scores, individual medical records, etc.); and so on. Thecomputer system 110 can identify the locations, sets of individuals, orclasses of individuals (e.g., user profiles or behavior profiles forindividuals) that most need vaccinations. Other recommendations for acommunity include recommendations of testing, including identificationof highest-exposure groups or locations to provide testing kits.

The computer system 110, through the geofencing and location trackinganalysis, can identify specific exposure events (e.g., represented byvisit-based location tags), the people potentially affected (e.g., thosewho concurrently or later visit the tagged, geofenced areas), as well asthe degree affected (e.g., exposure scores for the visits to the tagged,geofenced areas. The computer system 110 can provide this information tocommunity health organizations and governments, to assist in contacttracing and in characterizing the patterns of exposure that are presentin the community. Thus, the system 110 can provide contact tracing styledata indicating exposure events that meet certain thresholds orcriteria. In addition, or as an alternative, data indicating aggregatemeasures of exposure for the community, including patterns, trends, andstatistical measures of exposure, can be provided.

The process 2200 can optionally include selecting or adjusting diseasemonitoring, prevention, testing and treatment for individuals based ontheir disease exposure scores (step 2214). Not only can the computersystem 110 select a disease monitoring action and communicate it as arecommendation, the computer system 110 can often carry out actions toimplement needed actions to initiate treatment for COVID-19, changemonitoring or testing, and so on. Thus the computer system 110 mayimplement recommended actions for individuals by providing digitaltherapeutics, scheduling vaccine appointments, initiating delivery oftesting kits, and so on.

For example, based on a user's disease exposure score satisfying athreshold, the computer system 110 can adjust monitoring for COVID-19for a user, such as by providing new EMAs or surveys, changing thecollection of sensor data, and so on. Based on the disease exposurescore the system can provide treatment for COVID-19, such as throughdigital therapeutics classified as addressing COVID-19 symptoms or risk,through medications, and so on. For example, the disease exposure scorefor a user can be an input that affects the generation of the userscores 240 such as infection likelihood prediction, as well as theselection of a vaccine, digital therapeutic, data monitoring process fora user, and more (see FIG. 5).

In a similar manner, the computer system 110 can carry out recommendedactions at the community level, such as providing testing kits torecommended areas or individuals, initiating communication to instructchanges in disease prevention measures for members of the community(e.g., for the community as a whole or selectively for areas or groupsof individuals with the highest disease transmission scores or diseaseexposure scores), and so on.

In general, in addition to or instead of selecting and performingactions based on an exposure score representing an aggregate orcumulative level of exposure, the computer system 110 can select andperform actions in response to detection of specific risks orinteractions. For example, the computer system 110 can send data causinga user's mobile device to provide an intervention based on entry to asingle location tag, or to a single location where the location tag(s)combine to present at least a minimum level of risk. For example, upondetecting that a user has entered a geofenced area for a visit-basedlocation tag, the computer system can initiate an exposure notification,an exposure risk notification, a disease management or treatmentinstruction or recommendation, etc.

Although the discussion of the process 2200 is focused on determiningexposure levels and exposure risks for actions that have occurred,similar techniques can be used detect and notify of current or predictedexposure events. The system 110 detect entry to a geofenced area for alocation tag and warn of the current presence of someone with a case ofCOVID-19, or of the presence of a person with a case of COVID-19 havingbeen present within a threshold period of time (e.g., the previous 12hours). As noted above, location tags can be associated with (e.g., caninclude or be marked, labeled, linked to metadata, etc.) informationindicating whether the person whose visit the location tag representshas tested positive for COVID-19. Location tags can also be associatedwith infection likelihood predictions, or have disease transmissionscores based on the infection likelihood predictions, allowing thesystem to warn others of likely exposure (e.g., probable cases) evenbefore actual test results are available.

As another example, when a user enters a location or region where acorresponding disease transmission score exceeds a threshold, then theuser can be warned of the exposure (e.g., exposure that has occurred ormay occur) with a notification on the user's phone or other device. Thedisease transmission score can be for an individual location tag forwhich the user has entered the corresponding geofence, or for anaggregation of multiple location tags applicable to the location (e.g.,a combination of multiple visit-based location tags, or a combination ofvisit-based, population-based, and location-type-based tags). Asdiscussed further with respect to FIG. 23, the computer system 110 mayalso anticipate a user's plan or path leading to entry to a region witha high disease transmission score, and may warn the user or in advanceof the user's arrival, to prevent exposure and not merely notify theuser of exposure that has occurred.

FIG. 23 is a flow diagram showing an example of a process 2300 forpredicting future user actions and taking steps to limit or avoiddisease exposure due to those actions. The process 2300 can be performedby one or more computers, such as the computer system 110. In general,the operations of the process 2300 can be performed by one or moreservers, one or more client devices, or a combination of them. Inaddition to the operations illustrated, the process 2300 can optionallyinclude other operations discussed with respect to FIG. 11. In theexample, the disease mentioned can be COVID-19, or it can be anotherinfectious disease.

The process 2300 can be used to predict user actions that pose a healthrisk, to initiate interactions to confirm or characterize the potentialuser actions and the associated health risk, and to provideinterventions to lessen the health risk for the individual. As discussedabove, the computer system 110 can collect a rich set of monitoring datafor individuals and communities. The computer system 110 can use thiscollected data to predict future actions of users, for example, todetect that a specific user is likely to perform a specific action ortype of action in the future. The computer system 110 can use thisprediction capability to anticipate when a user is likely to act amanner that presents a health risk, such as a disease exposure risk forCOVID-19. The computer system 110 can then respond with communicationsor other interventions to limit or avoid the disease exposure. Forexample, the computer system 110 can warn the user of a disease exposurerisk, suggest a modification to reduce the risk (e.g., wearing a mask,limiting the duration of a visit, etc.), recommend an alternative actionwith lower risk (e.g., visiting at a different time, visiting a locationwith lower disease exposure risk, etc.), or take other actions.

The computer system 110 can detect conditions indicative of likelyfuture action by a user, send a personalized EMA or survey asking aboutthe user's plans, and based on the user's response initiate aninteraction to reduce a disease exposure risk for the user. For example,if the user is approaching a store or other populated area, the system110 can predict the user's destination and activity (e.g., shopping,exercising at a gym, etc.). The computer system 110 can ask about thepredicted destination and the nature of the visit (e.g., plannedduration, activity, etc.). Based on the user's response, the computersystem 110 can provide interventions to reduce or avoid diseaseexposure, such as to instruct disease prevention measures or discouragethe planned action.

These techniques can be used to predictively provide interventions withdifferent timing with respect to a user's planned action. The computersystem 110 can provide just-in-time interventions, such as thosetriggered by the user's context or provided shortly before a riskyaction by the user is expected. In other words, in some cases theinterventions can be provide when the user action is imminent or isexpected within a brief period of time (e.g., an hour, 30 minutes, 15minutes, 5 minutes, 1 minute, etc.). The computer system 110 can provideinterventions father in advance, such as hours or days in advance, basedon data such as behavior patterns of the user, calendar data indicatingscheduled appointments for the user, and so on.

The ability to predict and address future user actions before the userbegins them provides significant advantages for supporting health andwellbeing. The computer system 110 is able to provide interventionsduring or in response to a user activity. However, once a user has begunan activity the user is usually much less likely to change plans andstop the activity to avoid a health risk. For example, once a user hastraveled to a location and/or entered a location where there is adisease exposure risk, the user is much less likely to change plans andleave to avoid potential disease exposure. In addition, even if the useris willing to alter his behavior, it may be too late to make appropriatepreparations or precautions once a user has arrived at a location or hasbegun an activity. For example, an intervention, provided upon a userentering a location, that instructs the user to wear a mask may beineffective if the user did not remember to bring a mask. By contrast,by predicting user actions, confirming the user's plans, and providingan intervention well in advance, such as at the beginning of the day oras the user is leaving her house, the computer system 110 can enablemuch more likely adoption of recommendations and change in behavior. Asa result, the system 110 can be configured to anticipate future useractions and provide instructions, before they arrive at a locationinvolving a health risk and before they begin an activity that carries ahealth risk.

The recommendations and interactions that the system provides to avoiddisease exposure may vary significantly based on type of activitypredicted. For example, rather than simply stating a genericrecommendation (e.g., “remember to wear a mask”) every time the userleaves the house, the system can predict a location and/or activity fora future user action (e.g., grocery shopping at a certain store) andprovide customized interactions for that predicted action's risk profile(e.g., “Store X is safer than Store Y for grocery shopping,” or“remember to clean the handle of your shopping cart when you arrive.”)

In addition, initiating interactions to confirming the user's plannedaction and the associated circumstances is also important to providehigh-quality, useful interactions. For example, a reminder to a user towear a mask, sent every time a user leaves the house, is likely tobecome annoying or be ignored or blocked by the user. By contrast,recommendations and interactions that are tailored for the user'sspecific needs and specific activities can be much more likely to beeffective. Confirming the nature of the planned activity, e.g., purpose,duration, location, etc. can allow for much more customizedrecommendations. Interactions to request information from the userregarding potential future activities can serve multiple purposes, forexample, to confirm user intent (e.g., is the user actually planning onperforming the predicted activity), and characterize the activity andrefine the risk potential for the activity. For example, the user mayindicate that masks or other preventative measures are being used, whichmay lower the overall risk of the activity to an acceptable level.

The computer system 110 can predict a user's future activities based onvarious inputs, such as historical behavior patterns for the user,context data for the user, behavior of other users in the community,calendar data for the user, and so on. Context data indicating a currentcontext of the user, for example, location tracking data indicating alocation of the user's device, a speed and trajectory of the user'sdevice, a path traveled by the device, etc. The speed, direction, andpath that a person is traveling can indicate a likely destination for auser, especially when historical location tracking data shows the userhas travelled the same path to the same location before.

The computer system 110 can also predict a potential future action bythe user using historical information about the user, such as priortracked actions showing prior visits to a particular location or otherinformation indicating a behavior pattern for the user. For example, auser's location history may show that the user has visited a shoppingmall several times in the last month. The computer system 110 can obtaincurrent location tracking data for the user that indicates that the useris currently on route to the shopping mall, for example, due to theuser's phone travelling in a direction toward the shopping mall, theuser's phone being located within a certain level of proximity of theshopping mall (e.g., within half a mile), the user's phone being trackedalong a route or path previously used to visit the shopping mall, theshopping mall being set as a destination for a navigation application,etc.

The computer system 110 can also predict a potential future action bythe user based on behavior patterns of others, such as individuals inthe user's community or other individuals having similar characteristicsas the user. When the user's movement pattern (e.g., route, location,etc.) matches or is similar to the pattern that other users took toreach the location, the system can determine

The computer system 110 can also predict a potential future action bythe user based on calendar data, for example, appointments scheduled forlater in the day. The computer system 110 may initiate communication anddisease prevention interactions based on the calendar data alone, orbased on using the calendar data along with other information (e.g.,tracked context information) to verify that the user is proceedingtoward the scheduled appointment.

In some implementations, the process 2300 can be used to providejust-in-time interventions responsive to the context of the user. Forexample, the computer system 110 can monitor the user's context in realtime or near real time, and detect when an when an action by the user isimminent. Different triggers for various types of actions can be learnedthrough the analysis of historical data for the user, e.g., to identifywhat movement patterns, data inputs, device usage patterns, etc.typically precede different types of action by the user. Similarly, thetriggers can be determined based on analysis of the actions of otherusers. Changes in the context of the user, such as change in location orpath of movement of the user's device, can signal that a certain actionby a user is likely. Similarly, movement toward a location (e.g., gym,movie theater, etc.) can increase the likelihood of activities performedat the location. The computer system 110 can gather various signals of auser's potential future activity (e.g., prior behavior pattern,increasing proximity to a relevant location, etc.) and determine aconfidence score indicating a likelihood of the future action. When theconfidence score indicates a likelihood of that activity being performedin the future reaches a threshold, and/or that other conditions are met,such as a disease transmission metric and/or a user susceptibilitymetric indicate a risk above a threshold, the system can initiatecommunication (e.g., an EMA-type message regarding prospective actionsof the user) and recommendations to avoid the disease transmission riskpresented.

The process 2300 includes detecting a trigger based on data for anindividual (step 2302). The computer system 110 evaluates collected dataabout a user, for example, and determines whether any of one or moretriggering conditions are present. The detection of the trigger may bebased on receiving user data indicating a potential action of a user ofa mobile device. This user data can be context data for the user (e.g.,movement data, location tracking data, etc.), self-reported data,calendar data, etc. In some cases, a trigger is detected based ondetermining that the user's device is entering or is near a high-riskarea (e.g., based on location type, tagged exposure events, population,etc.).

For example, the computer system 110 can store records of prior behaviorpatterns of the individual of other individuals, and determined when thecurrent context or a pattern of recent action matches the contexts orpatterns that preceded the different types of user actions. When thecomputer system 110 determines that the similarity reaches a thresholdlevel, the computer system 110 can trigger the remaining actions of theprocess 2300.

In general, the computer system 110 can determine a prediction of aparticular type of activity that the individual is likely to perform,e.g., grocery shopping, exercising at a gym, attending a movie at atheater, etc. The trigger can be determining that the pattern ofbehavior, context, or other information collected for the user indicateswith a particular confidence level that the user will perform that typeof activity.

The prediction can additionally or alternatively be associated with aspecific location, such as to go shopping at a particular store. Whenthe prediction is associated with a location, the computer system 110can use information about the location, including predictions of machinelearning models (see FIGS. 11 and 19), based on location tags (see FIG.22), community disease measures, and so on.

In some implementations, the computer system 110 may assess both (i) thelikelihood of an activity of a user and/or the user's destination and(ii) the potential disease exposure risk for that activity and/orlocation in determining whether a trigger for further interaction andintervention is reached. For example, a sliding scale may quantify atradeoff between likelihood and exposure risk. For example, a predictedactivity with a high level of risk of contact with other (e.g., groceryshopping) may trigger further interactions and interventions even if theprobability determined is relatively low (e.g., 30-40%). A predictedactivity with a low risk (e.g., jogging outdoors) may not triggerinteractions even if the likelihood of occurring is high (e.g., 70-80%).On the other hand, even a low risk activity may trigger interactions ifthe predicted location presents a high risk (e.g., a populated area, anarea with high disease prevalence, an area with location-based tagsindicating disease exposure, etc.).

As an example, the computer system 110 can determine a risk score for apredicted activity type, a risk score for a predicted location for theactivity (e.g., disease transmission score for the location), and apredicted likelihood for the activity and/or location. The computersystem 110 can then combine (e.g., add them together, determine aweighted average, etc.) the risk scores and likelihood scores to obtaina combined score. The combined score can be compared to a threshold orother criteria to determine. In other implementations, scores foractivity risk, location risk, and likelihoods are compared to separatethresholds, to determine whether interaction with the user and/ordisease prevention actions are needed. This analysis can be done foreach of various potential activities and locations that are relevant tothe user, based on the current context of the user and the historicalactions of the user.

The process 2300 includes prompting the individual about future plansusing content determined based on the detected trigger (step 2304). Forexample, the computer system 110 can send a transmission causing theuser's mobile device to provide a question, EMA, survey, interactive UI,or other element that prompts the user to provide user about his futureplans, and often about a specific prospective action and/or locationthat the computer system 110 predicted. The prompt can be forinformation describing or confirming the user's intentions or futureplans. As a result, the interaction can be similar to an EMA, but ratherthan request information about the user's current context andexperience, it can have a forward-looking focus to ask about a futureevent that is planned or likely. The prompt can request a prediction orestimate from the user (e.g., an amount of time the user is expected toperform an activity or stay in a location, an expected destination,etc.).

In general, the prompt can be for information that characterizes ordescribes a prospective action or activity a user intends to perform,whether or not the specific activity is predicted by the computer system110. For example, certain changes in context or data patterns mayindicate that action by the user is likely (e.g., some activity islikely to occur within a predetermined time frame), but may not specifythe specific type or nature of action by the user. The prompts can thusrequest the information about a user's future plans, triggered by a datapattern, event, context, or other trigger criteria that may not indicatea specific predicted action or activity. Thus, while the triggers maycorrespond to specific actions or activities, the triggers mayalternatively represent patterns or contexts which are correlated withor are predictive of future exposure risks of the user even if thespecific actions are not defined or predicted. The triggers and/orpredictions can be based on likelihood of activity or action within somedefined period of time, such as a day, within 12 hours, within 4 hours,within 1 hour, within 15 minutes, within 5 minutes, etc.

Prompting the individual can include the computer system 110 providingcontent configured to cause the mobile device associated with the userto present a prompt for user input. The content can prompt for entry ofthe desired type of data with a question, a request for input,interactive controls, and so on. The prompt can be for user input tocharacterize a prospective action of the user. For example, the promptmay ask the user to confirm if an action is going to be performed, toindicate a duration of the action, the reasons for the activity, anindication of conditions at the proposed destination, preventativemeasures of the user, etc. Examples include “Do you plan to shop atStore A today?,” “How long do you plan to spend at store B?,” “Do youwear a mask when you go shopping?,” “What is the purpose for your triptoday?,” “Is Store C your destination on this trip?,” and so on. Theprompts can include text entry fields, buttons to select predeterminedoptions, and other means of receiving user input.

The content of the prompt can be determined based on the trigger thatwas detected or a predicted action or activity. The computer system 110then prompts for information about the predicted activity. For example,if the computer system 110 detects that the user is approaching a store,and so is likely to shop at that store, the computer system 110 cantailor the prompt to ask for information relevant to the activity (e.g.,shopping) and/or location (e.g., the particular store being approached).

The process 2300 includes receiving a response to the prompt (step2306). This can include, for example, receiving user input to a touchscreen, receiving voice input, etc. For example, a user may type ananswer to a question, select a button indicating whether the action isplanned to be performed or not, move a slider control to indicate aplanned duration, etc. As discussed above, the response can indicate anyof various aspects about the user's plans, such as: to confirm or denythat the prospective action will be performed; to indicate a differentaction or activity is planned; to indicate characteristics of theprospective action, including a location, duration, group of peopleinvolved, activity to be performed, etc.; to indicate conditions at thelocation; and so on.

The process 2300 includes evaluating disease exposure potential for theindividual based on the response (step 2308). The level of diseaseexposure can be for the prospective action or activity predicted, if theuser response(s) are consistent with or confirm the prediction. In othercases, the user response may indicate an action or activity that is avariation of or is completely different from the predicted activity. Forexample, the computer system 110 may detect, based on location data or acalendar entry that a user is planning on going to a mall, and maypredict the user will be shopping, but the user response to a prompt mayindicate that the user instead intends to visit a gym at the mallinstead. In this example, the system was correct in predicting thattravel and a visit to the general area would occur, but did notaccurately predict the specific location or activity. When the responseindicates a new action or activity intended by the user (e.g., an actionor activity with changed parameters compared to the prediction, or adifferent action or activity altogether), the computer system 110evaluates the disease exposure potential for the new action or activity.In some cases, if the user indicates that the prediction is incorrect(e.g., the user is actually going home rather than shopping, or isn'tactually leaving the current location), the computer system 110 maydetermine that no intervention or further processing to limit risk isneeded.

The system may determine a level of risk when evaluating potentialtriggers, the received information from the user may provide additionalinformation that adjusts the calculation. For example, the user mayconfirm that the action is planned, indicating a likelihood ofessentially 100%. The user may respond and indicate a planned durationof an activity, and so the risk for a 15 minute visit will be calculatedto be much less than for a 3 hour visit. Similarly, the user responsemay indicate aspects such as: a destination different from the predictedone; an activity different from the predicted one; conditions at thedestination; whether preventative measures like masks are used; and soon. Based on any or all of these factors, the computer system 110updates its risk scoring for the planned activity to reflect the currentinformation, and especially the user-confirmed information.

The evaluation of disease exposure potential can take into accountfactors such as the location tags for population, location type, andactual exposure. For example, a disease transmission score for alocation, based on one or more types of location tags, can bedetermined. As another example, different location types can havedifferent risk scores, e.g., a risk score of 9 for a hospital, a riskscore of 6 for a post office, and so on. The evaluation of diseaseexposure potential can take into account the type of action or activitythat the user intends to perform. For example, stored data may indicaterisk scores for different activities that may contribute differentlevels of risk, e.g., a risk score of 5 for shopping, a risk score of 8for exercising at a gym, etc. Similarly, the evaluation of diseaseexposure potential can take into account the user's personalsusceptibility to the disease (e.g., based on a score from a machinelearning model, or based on medical history for the user). In general,different locations or location types and different types of actions oractivities can have different levels of risk associated with them, andthe computer system 110 can store mapping data that indicates the levelsof risk. Those levels can be updated or enhanced based on the particularcircumstances (e.g., the location tags for exposures at locations) andbased on a user's personal level of susceptibility to the disease. Acombined score for various factors can be determined, for example, witha summation, selecting the maximum value among multiple componentscores, applying an equation or function, etc.

In some implementations, a machine learning model is trained to predictdisease exposure potential, based on an activity type and/or locationtype. This model can be trained based on the activities andcorresponding locations for different users over time, and thecorresponding instances of disease infection that occurred, to predictthe relative risk or likelihood of disease transmission for differentactivities and location types. The model may further be trained to takeinto account other contextual factors related to activities andlocations, such as whether disease prevention measures are used, levelsof population at the locations, characteristics of the locations, and soon. The input to the model can be based on the responses to the prompt,as well as any other collected data for the user, including context dataor other data evaluated to determine if a trigger for the process 2300is detected.

The process 2300 includes selecting a disease exposure prevention optionbased on the evaluation (2310). The determination to select and providean intervention to reduce disease risk can be conditioned on theexposure level or exposure risk being above a minimum level. In otherwords, if the evaluation indicates very low risk, no disease exposureprevention option need be selected and provided. On the other hand, whenthe disease exposure risk is above a threshold, the computer system 110selects an option to reduce the risk.

Selecting the disease exposure prevention option can include selectingfor the user that is predicted to reduce or avoid exposure of the userto the disease, wherein the disease exposure prevention option isselected or customized for the user based on at least one of the userdata or the response data. The computer system 110 can store dataindicating different options, such as to avoid the planned activity, towear a mask, to perform the activity at an alternative location, todelay the planned activity, etc. Based on the disease exposure potentialor risk level of the planned activity and the expected measure ofdecrease in risk for the different options, the computer system 110select and recommends one or more of the options. For example, when theevaluation indicates high risk, the computer system may recommend tocancel the planned activity or may recommend a lower-risk alternative.If the evaluation indicates a moderate risk, or one that can be loweredto an acceptable level using a mask or other measure, the computersystem 110 recommends the predicted measure(s) that can lower the risklevel sufficiently.

In some implementations, the disease prevention option is selected basedon output of a machine learning model. The output can be generated inresponse to the model receiving data indicating at least one of theprospective action or activity of the user, a location or location typefor the prospective action, and collected data for the user (e.g., userdisease status (e.g., vaccinated, recovered, etc.), behavior patterns,physiological measurements, user profile, etc.). The machine learningmodel can be one trained based on training data showing examples ofactions performed with and without disease prevention measures, and thecorresponding disease outcomes (whether at an individual or communitylevel), so that the model learns the levels of transmission riskreduction provided by different disease prevention measures in differentsituations (e.g., for different locations, activities, types of users,etc.)

In some implementations, the disease prevention option is selected basedon mapping data indicating predetermined disease prevention options thatcorrespond to different contexts, activities, locations, and/or exposurerisk levels. For example, stored mapping data can specify a preventativemeasure corresponding to the activity type of the prospective action, alocation type for a location of the prospective action, a diseasetransmission level for the location and/or prospective action, a userdisease susceptibility measure for the user, and/or user characteristicsof the user. In other words, the mapping data associates differentdisease prevention options with different values of one or more ofactivity types, location types, disease transmission levels, userdisease susceptibility measures, and/or user characteristics. Then,based on the various collected data for the user, including responses toprompts, the computer system 110 can look up the most applicable diseaseprevention option. In a simple example, there may be a set of diseaseprevention options associated with a particular activity, such asshopping, and the specific disease prevention option can be selected canbe based on the exposure risk level (e.g., exposure risk level less than10—stay 6 feet from others; exposure risk level between 10 and 20—wear amask; exposure risk level greater than 20—stay home or go to a storethat provides a lower risk level).

Many different disease prevention options are available to berecommended by the system. A few examples include: wearing personalprotective equipment, including at least one of a mask or gloves;maintaining a distance between the user and others; limiting a durationof the prospective action of the user; changing a location of theprospective action of the user; replacing the prospective action of theuser with a lower-risk activity; or avoiding the prospective action.

The process 2300 includes providing output data indicating the selecteddisease exposure prevention option (step 2312). For example, the systemcan provide content configured to cause the mobile device associatedwith the user to present the selected disease exposure preventionoption. For example, the computer system 110 may indicate, “Instead ofgoing to the gym where COVID-19 risk is high, try running outdoorstoday.” As another example, the computer system 110 may inform a user,“The mall is busy today and people diagnosed with COVID-19 were therethis morning. Can you postpone your trip?.” Other examples include“Please limit your visit to less than 30 minutes,” “Make sure to wearyour mask while you are out,” and “Shop at Store A instead of Store B.There was a confirmed COVID-19 exposure at Store B.”

In general, the process 2300 can be used to detect potential futureactions or activities that present health risks more generally, not onlyfor risks of disease exposure. Similarly, the options selected andcommunicated to the user can be options determined to reduce or avoidthe type of health risk detected. For example, if a user is recoveringfrom foot surgery, certain activities or exercises can present a risk ofinjury. The system may detect locations, movements, calendarappointments, and other that indicate an activity that presents a highrisk of re-injury given the user's condition, and then use the processto ask the user about his plans and provide interventions to reduce oravoid the risk. As another example, a user that is recovering fromalcohol addiction may have a risk of substance abuse. The computersystem 110 can evaluate the movement patterns, context, calendarappointments, and so on to determine when there is a risk that the userwill consume or purchase alcohol and may use the process to select andprovide interventions to reduce or avoid the risk. For example, if auser has a party scheduled on a calendar or is in proximity to a liquorstore the system can weigh the risk level and likelihood of purchasingor consuming alcohol, send one or more interactions to find out moreabout the user's intent and context, and then if appropriate initiate anintervention (e.g., to discourage the user, to recommend an alternative,to encourage support of a family member or friend, etc.).

The data collected by the computer system 110 and used in any of theexamples and implementations discussed above can include a variety ofinformation from a variety of sources. Data can be collected forcategories representing a variety of individual, community, or publichealth conditions and behaviors. This data can include attributes thatare biological, physical or physiological, mental, emotional,environmental, or social. The collected data can include biologicalattributes, such as genetic makeup, genomics, family history, sensoryabilities (e.g., ability to see, perception of light and dark,perception of color, extent of ability to smell, ability to touch andsensitivity, ability to hear and sensitivity, etc.). These may reflectbiological factors that a person cannot control. The collected data caninclude physical or physiological attributes, e.g., weight, muscle mass,heart rate, sleep, nutrition, exercise, lung capacity, brain activity,etc. Some physical attributes may result from the impact of lifestylechoices or things that a person can control. The collected data caninclude mental attributes, such as interpretation of brain relatedsignals, indications of chemical imbalances, education levels, resultsof mental tests, etc. The collected data can include emotionalattributes, such as interpretation of self-reported data, or classifiedaudio or video related data that suggests individual responses tostimuli. The collected data can include environmental data, such aslocation data, air quality, audible noise, visual noise, temperature,humidity, movement (and potentially effects of movement such as motionsickness, etc. The collected data can include social attributes, such aswhether a subject is socially engaged, exhibits social avoidance,experiences the impact of acceptance or responsiveness emotionally, andso on.

The data collected and used by the computer system 110 (e.g., togenerate feature values for input to predictive models, to trainpredictive models, to validate and select actions and recommendations,to evaluate to determine whether to initiate interactions with users, toassign or determine disease transmission and exposure scores, etc.) caninclude various other types of data including:

-   -   Lab and diagnostic data (e.g., assay data, blood test results,        tissue sample results, endocrine panel results);    -   Omics data (e.g., data relating to genomics, proteomics,        pharmacogenomics, epigenomics, metabolomics, biointeractomics,        interactomics, lifeomics, calciomics, chemogenomics, foodomics,        lipidomics, metabolomics, bionomics, econogenomics,        connectomics, culturomics, cytogenomics, fermentanomics,        fluxomics, metagenomics, metabonomics, metallomics,        O-glcNAcomics, glycomics, glycoproteomics,        glycosaminoglycanomics, immunoproteomics, ionomics, materiomics,        metalloproteomics, metaproteogenomics, metaproteomics,        metatranscriptomics, metronomics, microbiomics, microeconomics,        microgenomics, microproteomics, miRomics, mitogenomics,        mitoproteomics, mobilomics, morphomics, nanoproteomics,        neuroeconomics, neurogenomics, neuromics, neuropeptidomics,        neuroproteomics, nitroproteomics, nutrigenomics,        nutrimetabonomics, oncogenomics, orthoproteomics, pangenomics,        peptidomics, pharmacoeconomics, pharmacometabolomics,        pharmacoproteomics, pharmaeconomics, phenomics,        phospholipidomics, phosphoproteomics, phylogenomics,        phylotranscriptomics, phytomics, postgenomics, proteogenomics,        proteomics, radiogenomics, rehabilomics, retrophylogenomics,        secretomics, surfaceomics, surfomics, toxicogenomics,        toxicometabolomics, toxicoproteomics, transcriptomics,        vaccinomics, variomics, venomics, antivenomics, agrigenomics,        aquaphotomics);    -   Biologically sampled data (e.g., data describing blood, urine,        saliva, breath sample, skin scrape, hormone levels, ketones,        glucose levels, breathalyzer, DNA, perspiration, and other        biological samples and derived data);    -   Cardiac-related biodata (e.g., data from ECG/EKG monitors, heart        rate monitors, blood pressure monitors);    -   Respiratory-related biodata (e.g., data from spirometers, pulse        oximeters);    -   Neurological-related biodata (e.g., data from EEG monitors);    -   Behavior data (e.g., movement patterns, gait, social avoidance);    -   Drug data (e.g., prescription information, pharmacological        data);    -   Substance use data (e.g., alcohol, medication, insulin,        recreational drugs, tobacco);    -   Sleep data (e.g., motion data, heart rate data, body        temperature, perspiration, breathing data, ambient light,        ambient sound, ambient temperature);    -   Exercise data (e.g. performance data, distance covered,        activity, VO2 Max),    -   Physical activity data (e.g., step counts, heart rate, flights        climbed, altitude, other data from fitness trackers);    -   Mood data (e.g., happiness, depression, PHQ9, BMIS data and        other scales/reporting mechanisms);    -   Positioning and location data (e.g., GPS data, gyroscope data,        altimeter data, accelerometer data, linear acceleration data,        received signal strength indicator from nearby emitters such as        Wi-Fi access points, Bluetooth sensors, sensor networks, and        cellular towers);    -   Environmental data (e.g., air quality data, ozone data, weather        data, water-quality data, audible decibel levels, interpreting        measured audio data, measuring luminance lux, interpreting        measured light wavelengths, measuring temperature and gases or        particles—such as formaldehyde (Molecular Formula: H₂CO or        CH₂O); alcohol vapor (Molecular Formula: hydroxyl group-OH,        e.g., IsopropylC₃H₈O or C₃H₇OH, as well as Ethanol: C₂H₈O or        C₂H₅OH); benzene (C₆H₆), Hexane (C₆H₁₄), Liquefied Petroleum Gas        (LPG) which could include a mixture of butane (Molecular        Formula: CH₃CH₂CH₂CH₃ or C₄H₁₀) and isobutene (Molecular        Formula: (CH₃)₂CHCH₃ or C₄H₁₀ or (CHC₄H₁₀)₂CHCH₃), propane        (Molecular Formula: CH₃CH₂CH₃ or C₃H₈), natural coal or town gas        which could include of methane or natural gas (Molecular        Formula: CH₄), carbon dioxide (Molecular Formula: CO₂); hydrogen        (Molecular Formula: H₂); carbon monoxide or possibly smoke        (Molecular Formula: CO); and oxygen (Molecular Formula: O₂) in        the environment surrounding an individual inside and outside the        contextual location of the potential subjects such as home,        office, and including vehicle data—such as speed, location,        amount of time driving, mood while driving, environmental data        in the car).

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. For example, various formsof the flows shown above may be used, with steps re-ordered, added, orremoved.

Embodiments of the invention and all of the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe invention can be implemented as one or more computer programproducts, e.g., one or more modules of computer program instructionsencoded on a computer readable medium for execution by, or to controlthe operation of, data processing apparatus. The computer readablemedium can be a machine-readable storage device, a machine-readablestorage substrate, a memory device, a composition of matter effecting amachine-readable propagated signal, or a combination of one or more ofthem. The term “data processing apparatus” encompasses all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a tablet computer, a mobile telephone, a personaldigital assistant (PDA), a mobile audio player, a Global PositioningSystem (GPS) receiver, to name just a few. Computer readable mediasuitable for storing computer program instructions and data include allforms of non-volatile memory, media and memory devices, including by wayof example semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, embodiments of the invention canbe implemented on a computer having a display device, e.g., a CRT(cathode ray tube) or LCD (liquid crystal display) monitor, fordisplaying information to the user and a keyboard and a pointing device,e.g., a mouse or a trackball, by which the user can provide input to thecomputer. Other kinds of devices can be used to provide for interactionwith a user as well; for example, feedback provided to the user can beany form of sensory feedback, e.g., visual feedback, auditory feedback,or tactile feedback; and input from the user can be received in anyform, including acoustic, speech, or tactile input.

Embodiments of the invention can be implemented in a computing systemthat includes a back end component, e.g., as a data server, or thatincludes a middleware component, e.g., an application server, or thatincludes a front end component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the invention, or any combination ofone or more such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

In each instance where an HTML file is mentioned, other file types orformats may be substituted. For instance, an HTML file may be replacedby an XML, JSON, plain text, or other types of files. Moreover, where atable or hash table is mentioned, other data structures (such asspreadsheets, relational databases, or structured files) may be used.

Particular embodiments of the invention have been described. Otherembodiments are within the scope of the following claims. For example,the steps recited in the claims can be performed in a different orderand still achieve desirable results.

What is claimed is:
 1. A method performed by one or more computers, themethod comprising: training, by the one or more computers, a machinelearning model to predict a level of disease exposure that would resultfrom a user activity, the machine learning model being configured toreceive input data indicating at least an activity type for the activityor a location type for a location of the activity, the model beingtrained based on examples of (i) user activities and correspondinglocations of the user activities for different users and (ii)corresponding instances of disease infection that occurred for thedifferent users; identifying, by the one or more computers, aprospective activity of a user of a mobile device based on context dataindicating a current context of the mobile device of the user;detecting, by the one or more computers, that a trigger conditionassociated with the prospective activity is satisfied based onevaluation of the context data; in response to detecting that thetrigger condition associated with the prospective activity is satisfied,providing, by the one or more computers and to the mobile device over acommunication network, content configured to cause the mobile deviceassociated with the user to present a prompt determined based on theprospective activity, the prompt requesting user input to confirm ordescribe a plan of the user with respect to the prospective activity;generating, by the one or more computers, a measure of potential futureexposure of the user to a disease based on response data indicating aresponse to the prompt, wherein the measure of potential future exposureof the user to the disease is generated by processing, using the trainedmachine learning model, input indicating an activity type for theprospective activity or a location type for a location for theprospective activity to obtain a predicted level of disease exposure forthe user; selecting, by the one or more computers, a disease exposureprevention option for the user that is predicted to reduce or avoidexposure of the user to the disease, wherein the disease exposureprevention option is selected or customized for the user based on themeasure of potential future exposure of the user; and providing, by theone or more computers and to the mobile device over the communicationnetwork, content configured to cause the mobile device associated withthe user to present the disease exposure prevention option.
 2. Themethod of claim 1, wherein the disease is COVID-19.
 3. The method ofclaim 1, wherein the context data comprises location tracking data for amobile device associated with the user, wherein detecting that thetrigger condition is satisfied is based at least in part on the locationtracking data.
 4. The method of claim 1, comprising receiving a userinput to a second prompt provided by the mobile device, the secondprompt being provided before detecting that the trigger condition issatisfied, wherein detecting that the trigger condition is satisfied isbased at least in part on the user input to the second prompt.
 5. Themethod of claim 1, wherein the context data indicates a location or pathof movement for the user; wherein the detected trigger is detected basedon determining that the user has entered or is approaching a particularlocation visited, within a predetermined period of time, by a personclassified as having COVID-19; and wherein the prompt requestsinformation regarding an expectation of the user regarding a current orupcoming visit to the particular location.
 6. The method of claim 5,wherein the prompt requests information characterizing an expecteddestination, duration, or activity for the current or upcoming visit tothe particular location by the user.
 7. The method of claim 1, whereindetecting that the trigger condition is satisfied based on evaluation ofthe received context data comprises determining that an output generatedby a machine learning model based on the context data satisfies one ormore criteria associated with the trigger condition.
 8. The method ofclaim 1, comprising accessing calendar data for the user, whereinidentifying the prospective activity or detecting that the triggercondition is satisfied is based at least in part on an appointment forthe user indicated by the calendar data for the user.
 9. The method ofclaim 1, wherein detecting that the trigger condition is satisfiedcomprises: determining, based on (i) the context data and (ii)historical data indicating prior context and prior activities of theuser, a likelihood that the user will begin the prospective activitywithin a predetermined amount of time; and determining that thelikelihood that the user will begin the prospective activity within thepredetermined amount of time meets or exceeds a minimum threshold. 10.The method of claim 1, wherein detecting that the trigger condition issatisfied comprises determining that a level of disease exposure riskfor the prospective activity satisfies a threshold.
 11. The method ofclaim 1, wherein detecting that the trigger condition is satisfiedcomprises determining that a context indicated by the context data hasat least a predetermined minimum level of similarity with a priorcontext of the user or of one or more other users that precededperformance of the prospective activity.
 12. The method of claim 1,wherein the response data indicates or confirms an intended activity ofthe user, wherein the disease prevention option comprises at least oneof: wearing personal protective equipment, including at least one of amask or gloves; maintaining a distance between the user and others;limiting a duration of the intended activity of the user; changing alocation of the intended activity of the user; replacing the intendedactivity of the user with a lower-risk activity; or avoiding theintended activity; wherein selecting the disease prevention optioncomprises: selecting the disease prevention option based on output of amachine learning model generated in response to receiving dataindicating at least one of the intended activity, a location or locationtype for the intended activity, and collected data for the user; orselecting the disease prevention option that stored mapping dataspecifies as corresponding to the activity type of the intendedactivity, a location type for a location of the intended activity, adisease transmission level for the location and/or the intendedactivity, a user disease susceptibility measure for the user, and/oruser characteristics of the user; wherein the stored mapping dataassociates different disease prevention options with different values ofone or more of activity types, location types, disease transmissionlevels, user disease susceptibility measures, and/or usercharacteristics.
 13. The method of claim 1, wherein the prompt comprisesa prompt to: confirm whether the user intends to perform the prospectiveactivity or another activity; indicate a destination or mode of travelof the user; or describe conditions for the prospective activity,including at least one of a location type for the prospective activity,a location of the prospective activity, characteristics of a locationfor the prospective activity, a number of people at the location, orwhether disease prevention measures are used.
 14. The method of claim 1,comprising: monitoring, by the one or more computers, user actions of auser and user inputs to a mobile device of a user over time to generatemonitoring data for the user; determining, by the one or more computers,a plurality of triggers based on the monitoring data for the user,wherein the triggers indicate user actions and contexts that aredetermined, based on the monitoring data for the user, to precededifferent activities of the user; wherein determining that the triggercondition has been satisfied comprises detecting a particular trigger ofthe plurality of triggers, wherein the prospective activity is anactivity corresponding to the particular trigger.
 15. The method ofclaim 1, wherein the machine learning model being configured to receiveinput data indicating at least an activity type for the activity and alocation type for a location of the activity; and wherein the measure ofpotential future exposure of the user to the disease is generated byprocessing, using the trained machine learning model, input indicatingan activity type for the prospective activity and a location type for alocation for the prospective activity to obtain a predicted level ofdisease exposure for the user.
 16. The method of claim 1, whereintraining the machine learning model comprises training the machinelearning model to predict levels of disease exposure further based oncontextual information for activities or locations, the contextualinformation comprising at least one of (i) whether a disease preventionmeasure is used, (ii) a population level at a location, or (ii) acharacteristic of a location.
 17. The method of claim 1, whereinsatisfying the trigger condition indicates that a likelihood of the userperforming the prospective activity within a predetermined amount oftime in the future meets or exceeds a minimum threshold.
 18. A systemcomprising: one or more computers; and one or more computer-readablemedia storing instructions that, when executed by the one or morecomputers, cause the one or more computers to perform operationscomprising: training, by the one or more computers, a machine learningmodel to predict a level of disease exposure that would result from auser activity, the machine learning model being configured to receiveinput data indicating at least an activity type for the activity or alocation type for a location of the activity, the model being trainedbased on examples of (i) user activities and corresponding locations ofthe user activities for different users and (ii) corresponding instancesof disease infection that occurred for the different users; identifying,by the one or more computers, a prospective activity of a user of amobile device based on context data indicating a current context of themobile device of the user; detecting, by the one or more computers, thata trigger condition associated with the prospective activity issatisfied based on evaluation of the context data; in response todetecting that the trigger condition associated with the prospectiveactivity is satisfied, providing, by the one or more computers and tothe mobile device over a communication network, content configured tocause the mobile device associated with the user to present a promptdetermined based on the prospective activity, the prompt requesting userinput to confirm or describe a plan of the user with respect to theprospective activity for user input regarding the prospective action ofthe user; generating, by the one or more computers, a measure ofpotential future exposure of the user to a disease based on responsedata indicating a response to the prompt, wherein the measure ofpotential future exposure of the user to the disease is generated byprocessing, using the trained machine learning model, input indicatingan activity type for the prospective activity or a location type for alocation for the prospective activity to obtain a predicted level ofdisease exposure for the user; selecting, by the one or more computers,a disease exposure prevention option for the user that is predicted toreduce or avoid exposure of the user to the disease, wherein the diseaseexposure prevention option is selected or customized for the user basedon the measure of potential future exposure of the user; and providing,by the one or more computers and to the mobile device over thecommunication network, content configured to cause the mobile deviceassociated with the user to present the disease exposure preventionoption.
 19. One or more non-transitory computer-readable media storinginstructions that, when executed by the one or more computers, cause theone or more computers to perform operations comprising: training, by theone or more computers, a machine learning model to predict a level ofdisease exposure that would result from a user activity, the machinelearning model being configured to receive input data indicating atleast an activity type for the activity or a location type for alocation of the activity, the model being trained based on examples of(i) user activities and corresponding locations of the user activitiesfor different users and (ii) corresponding instances of diseaseinfection that occurred for the different users; identifying, by the oneor more computers, a prospective activity of a user of a mobile devicebased on context data indicating a current context of the mobile deviceof the user; detecting, by the one or more computers, that a triggercondition associated with the prospective activity is satisfied based onevaluation of the context data; in response to detecting that thetrigger condition associated with the prospective activity is satisfied,providing, by the one or more computers and to the mobile device over acommunication network, content configured to cause the mobile deviceassociated with the user to present a prompt determined based on theprospective activity, the prompt requesting user input to confirm ordescribe a plan of the user with respect to the prospective activity;generating, by the one or more computers, a measure of potential futureexposure of the user to a disease based on response data indicating aresponse to the prompt, wherein the measure of potential future exposureof the user to the disease is generated by processing, using the trainedmachine learning model, input indicating an activity type for theprospective activity or a location type for a location for theprospective activity to obtain a predicted level of disease exposure forthe user; selecting, by the one or more computers, a disease exposureprevention option for the user that is predicted to reduce or avoidexposure of the user to the disease, wherein the disease exposureprevention option is selected or customized for the user based on themeasure of potential future exposure of the user; and providing, by theone or more computers and to the mobile device over the communicationnetwork, content configured to cause the mobile device associated withthe user to present the disease exposure prevention option.
 20. A methodperformed by one or more computers, the method comprising: training amachine learning model based on training data providing (i) examples ofuser actions performed with different disease prevention measures andexamples of user actions performed without disease prevention measuresand (ii) data indicating disease outcomes corresponding to the examples,the machine learning model being trained to assess different levels ofdisease transmission reduction provided by the different diseaseprevention measures in different situations; identifying, by the one ormore computers, a prospective activity of a user of a mobile devicebased on context data indicating a current context of the mobile deviceof the user; detecting, by the one or more computers, that a triggercondition associated with the prospective activity is satisfied based onevaluation of the context data; in response to detecting that thetrigger condition associated with the prospective activity is satisfied,providing, by the one or more computers and to the mobile device over acommunication network, content configured to cause the mobile deviceassociated with the user to present a prompt determined based on theprospective activity, the prompt requesting user input to confirm ordescribe a plan of the user with respect to the prospective activity;generating, by the one or more computers, a measure of potential futureexposure of the user to a disease based on response data indicating aresponse to the prompt; selecting, by the one or more computers, adisease exposure prevention option for the user that is predicted toreduce or avoid exposure of the user to the disease, wherein the diseaseexposure prevention option is selected based on one or more outputs ofthe trained machine learning model generated by processing dataindicating at least one of the prospective activity or a location forthe prospective activity using the trained machine learning model; andproviding, by the one or more computers and to the mobile device overthe communication network, content configured to cause the mobile deviceassociated with the user to present the disease exposure preventionoption.